{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data104117\r\n"
     ]
    }
   ],
   "source": [
    "# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原\n",
    "# View dataset directory. \n",
    "# This directory will be recovered automatically after resetting environment. \n",
    "!ls /home/aistudio/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢.\n",
    "# View personal work directory. \n",
    "# All changes under this directory will be kept even after reset. \n",
    "# Please clean unnecessary files in time to speed up environment loading. \n",
    "!ls /home/aistudio/work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#PaddleDetection的代码库下载，同时支持github源和gitee源，为了在国内网络环境更快下载，此处使用gitee源。  \r\n",
    "# 默认的是2.0\r\n",
    "#! git clone https://github.com/PaddlePaddle/PaddleDetection.git\r\n",
    "# ! git clone https://gitee.com/paddlepaddle/PaddleDetection.git\r\n",
    "# 直接解压release2.1\r\n",
    "! unzip -oq /home/aistudio/data/data98540/PaddleDetection-release-2.1.zip\r\n",
    "#手动修改文件名为PaddleDetection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 将数据集fire.zip解压到PaddleDetection/dataset\r\n",
    "!unzip -oq data/data104117/fire.zip -d PaddleDetection/dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**数据集划分**\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/f37c9f02ab3645958244a1dc6b3bf1133f52295be0c34062a162b1a0c9979888)\n",
    "\n",
    "Annotations为标注文件\n",
    "\n",
    "JPEGImage为图片文件\n",
    "\n",
    "train.txt为训练图片的路径和对应的标注文件的路径\n",
    "\n",
    "val.txt为验证图片的路径和对应的标注文件的路径\n",
    "\n",
    "Label_list.txt为标签文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleDetection/dataset/fire\n",
      "/home/aistudio/PaddleDetection/dataset\n",
      "/home/aistudio/PaddleDetection\n"
     ]
    }
   ],
   "source": [
    "%cd PaddleDetection/dataset/fire\r\n",
    "#数据集划分\r\n",
    "import random\r\n",
    "import os\r\n",
    "#生成train.txt和val.txt\r\n",
    "random.seed(2020)\r\n",
    "xml_dir  = 'Annotations'\r\n",
    "img_dir = 'JPEGImages'\r\n",
    "path_list = list()\r\n",
    "for img in os.listdir(img_dir):\r\n",
    "    img_path = os.path.join(img_dir,img)\r\n",
    "    xml_path = os.path.join(xml_dir,img.replace('jpg', 'xml'))\r\n",
    "    path_list.append((img_path, xml_path))\r\n",
    "random.shuffle(path_list)\r\n",
    "ratio = 0.9\r\n",
    "train_f = open('train.txt','w') \r\n",
    "val_f = open('val.txt' ,'w')\r\n",
    "\r\n",
    "for i ,content in enumerate(path_list):\r\n",
    "    img, xml = content\r\n",
    "    text = img + ' ' + xml + '\\n'\r\n",
    "    if i < len(path_list) * ratio:\r\n",
    "        train_f.write(text)\r\n",
    "    else:\r\n",
    "        val_f.write(text)\r\n",
    "train_f.close()\r\n",
    "val_f.close()\r\n",
    "\r\n",
    "#生成标签文档\r\n",
    "label = ['fire']\r\n",
    "with open('label_list.txt', 'w') as f:\r\n",
    "    for text in label:\r\n",
    "        f.write(text+'\\n')\r\n",
    "\r\n",
    "# 返回PaddleDetection工作目录\r\n",
    "%cd ../\r\n",
    "%cd ../"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleDetection\n",
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/\n",
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      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/e6/ba/77120e44cbe9719152415b97d5bfb29f4053ee987d6cb63f55ce7d50fadc/py-cpuinfo-8.0.0.tar.gz (99kB)\n",
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      "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->pre-commit->visualdl>=2.1.0->-r requirements.txt (line 3)) (0.6.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.5->Flask-Babel>=1.0.0->visualdl>=2.1.0->-r requirements.txt (line 3)) (1.1.1)\n",
      "Requirement already satisfied: more-itertools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from zipp>=0.5->importlib-metadata; python_version < \"3.8\"->pre-commit->visualdl>=2.1.0->-r requirements.txt (line 3)) (7.2.0)\n",
      "Building wheels for collected packages: terminaltables, pycocotools, lap, py-cpuinfo\n",
      "  Building wheel for terminaltables (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp37-none-any.whl size=15356 sha256=d57c6af5dbd8bdf3ab746b5995678982da7a960c4c655d6ec4d666e314c57d3f\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/38/ce/e7/382f63c6888f05daac9bffbdea230dc620ceda20bedb449dce\n",
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      "\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=278365 sha256=8812228a314c3895c2eb83ff7780498529f478e3d5e188dcb2fb379769811a85\n",
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      "\u001b[?25h  Created wheel for lap: filename=lap-0.4.0-cp37-cp37m-linux_x86_64.whl size=1593872 sha256=bfbc8b58fce0dfe77119f07535741c32c5e358b71e429b5235f11eef0ab3517c\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/83/50/a9/e3660736bfb1fb50598b822551bb8c7ff04f1a4ecf69c42277\n",
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      "\u001b[?25h  Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-cp37-none-any.whl size=22245 sha256=546430f26a4c3f7b912c71968093d7297b2e17e04394f88697faceb49bc55b18\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/a5/77/fe/bd2fe25844956ae1e2353d7b2ffc9a90ab8c3c1b4b0862e1e9\n",
      "Successfully built terminaltables pycocotools lap py-cpuinfo\n",
      "Installing collected packages: typeguard, shapely, terminaltables, pycocotools, lap, py, iniconfig, pytest, xmltodict, py-cpuinfo, pytest-benchmark, flake8-import-order, motmetrics\n",
      "Successfully installed flake8-import-order-0.18.1 iniconfig-1.1.1 lap-0.4.0 motmetrics-1.2.0 py-1.10.0 py-cpuinfo-8.0.0 pycocotools-2.0.2 pytest-6.2.4 pytest-benchmark-3.4.1 shapely-1.7.1 terminaltables-3.1.0 typeguard-2.12.1 xmltodict-0.12.0\n"
     ]
    }
   ],
   "source": [
    "# 安装依赖\r\n",
    "%cd PaddleDetection/\r\n",
    "! pip install -r requirements.txt\r\n",
    "# ! pip install paddledet==2.1.0 -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**修改运行模型的相关参数**\n",
    "\n",
    "以yolov3，VOC数据集格式为例\n",
    "\n",
    "配置文件路径：work/PaddleDetection/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml\n",
    "\n",
    "修改work/PaddleDetection/configs/datasets/voc.yml文件：\n",
    "\n",
    "num_classes: 1\n",
    "\n",
    "TrainDataset:\n",
    "\n",
    "!VOCDataSet\n",
    "\n",
    "dataset_dir: dataset/fire\n",
    "\n",
    "anno_path: train.txt\n",
    "\n",
    "label_list: label_list.txt\n",
    "\n",
    "data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']\n",
    "EvalDataset:\n",
    "\n",
    "!VOCDataSet\n",
    "\n",
    "dataset_dir: dataset/fire\n",
    "\n",
    "anno_path: val.txt\n",
    "\n",
    "label_list: label_list.txt\n",
    "\n",
    "data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘../work/result_model’: File exists\r\n"
     ]
    }
   ],
   "source": [
    "# 创建一个保存模型的的位置\r\n",
    "!mkdir ../work/result_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if data.dtype == np.object:\n",
      "/home/aistudio/PaddleDetection/ppdet/data/reader.py:187: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead\n",
      "  logger.warn(\"Shared memory size is less than 1G, \"\n",
      "[08/13 14:24:29] reader WARNING: Shared memory size is less than 1G, disable shared_memory in DataLoader\n",
      "W0813 14:24:29.191963  7449 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0813 14:24:29.196687  7449 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[08/13 14:24:33] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/ResNet50_vd_ssld_pretrained.pdparams\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if data.dtype == np.object:\n",
      "[08/13 14:24:34] ppdet.engine INFO: Epoch: [0] [ 0/36] learning_rate: 0.000000 loss_xy: 1.417412 loss_wh: 5.812721 loss_iou: 5.091763 loss_iou_aware: 1.804805 loss_obj: 11300.642578 loss_cls: 20.658316 loss: 11335.427734 eta: 1:32:01 batch_cost: 0.5681 data_cost: 0.0004 ips: 21.1235 images/s\n",
      "[08/13 14:24:49] ppdet.engine INFO: Epoch: [0] [20/36] learning_rate: 0.000017 loss_xy: 1.526802 loss_wh: 5.249365 loss_iou: 5.496856 loss_iou_aware: 1.189128 loss_obj: 12778.065430 loss_cls: 21.202435 loss: 12817.785156 eta: 1:48:30 batch_cost: 0.6764 data_cost: 0.0003 ips: 17.7418 images/s\n",
      "[08/13 14:25:02] ppdet.engine INFO: Epoch: [1] [ 0/36] learning_rate: 0.000030 loss_xy: 1.584712 loss_wh: 5.117106 loss_iou: 5.469434 loss_iou_aware: 1.207813 loss_obj: 11807.284180 loss_cls: 21.411600 loss: 11835.263672 eta: 1:46:40 batch_cost: 0.6512 data_cost: 0.0003 ips: 18.4265 images/s\n",
      "[08/13 14:25:20] ppdet.engine INFO: Epoch: [1] [20/36] learning_rate: 0.000047 loss_xy: 1.694743 loss_wh: 5.780326 loss_iou: 5.985329 loss_iou_aware: 1.289740 loss_obj: 13747.757812 loss_cls: 23.642529 loss: 13793.290039 eta: 1:55:51 batch_cost: 0.8274 data_cost: 0.1811 ips: 14.5026 images/s\n",
      "[08/13 14:25:32] ppdet.engine INFO: Epoch: [2] [ 0/36] learning_rate: 0.000060 loss_xy: 1.452138 loss_wh: 5.440990 loss_iou: 5.562990 loss_iou_aware: 1.299839 loss_obj: 10420.809570 loss_cls: 22.301910 loss: 10453.294922 eta: 1:49:13 batch_cost: 0.5337 data_cost: 0.0003 ips: 22.4842 images/s\n",
      "[08/13 14:25:50] ppdet.engine INFO: Epoch: [2] [20/36] learning_rate: 0.000077 loss_xy: 1.711106 loss_wh: 4.594090 loss_iou: 5.445295 loss_iou_aware: 1.352493 loss_obj: 14665.540039 loss_cls: 21.208239 loss: 14704.194336 eta: 1:50:44 batch_cost: 0.7301 data_cost: 0.0003 ips: 16.4362 images/s\n",
      "[08/13 14:26:04] ppdet.engine INFO: Epoch: [3] [ 0/36] learning_rate: 0.000090 loss_xy: 1.814084 loss_wh: 5.524298 loss_iou: 5.920926 loss_iou_aware: 1.325384 loss_obj: 11113.714844 loss_cls: 22.109684 loss: 11162.343750 eta: 1:51:25 batch_cost: 0.6823 data_cost: 0.0257 ips: 17.5879 images/s\n",
      "[08/13 14:26:22] ppdet.engine INFO: Epoch: [3] [20/36] learning_rate: 0.000107 loss_xy: 1.538289 loss_wh: 4.937901 loss_iou: 5.708370 loss_iou_aware: 1.164366 loss_obj: 10090.087891 loss_cls: 21.411528 loss: 10130.900391 eta: 1:52:18 batch_cost: 0.7407 data_cost: 0.0003 ips: 16.2012 images/s\n",
      "[08/13 14:26:35] ppdet.engine INFO: Epoch: [4] [ 0/36] learning_rate: 0.000120 loss_xy: 1.475898 loss_wh: 4.794668 loss_iou: 5.416574 loss_iou_aware: 1.135950 loss_obj: 7645.015625 loss_cls: 20.822596 loss: 7675.105469 eta: 1:51:38 batch_cost: 0.6900 data_cost: 0.0302 ips: 17.3918 images/s\n",
      "[08/13 14:26:52] ppdet.engine INFO: Epoch: [4] [20/36] learning_rate: 0.000137 loss_xy: 1.328705 loss_wh: 4.432702 loss_iou: 5.070841 loss_iou_aware: 1.034515 loss_obj: 6104.345703 loss_cls: 19.289204 loss: 6134.828125 eta: 1:50:59 batch_cost: 0.6778 data_cost: 0.0003 ips: 17.7053 images/s\n",
      "[08/13 14:27:07] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:27:07] ppdet.engine INFO: Epoch: [5] [ 0/36] learning_rate: 0.000150 loss_xy: 1.469959 loss_wh: 4.674291 loss_iou: 5.474060 loss_iou_aware: 1.103755 loss_obj: 4675.657227 loss_cls: 20.482990 loss: 4713.287109 eta: 1:49:55 batch_cost: 0.6225 data_cost: 0.0003 ips: 19.2786 images/s\n",
      "[08/13 14:27:23] ppdet.engine INFO: Epoch: [5] [20/36] learning_rate: 0.000167 loss_xy: 1.500635 loss_wh: 4.746734 loss_iou: 5.540401 loss_iou_aware: 1.085176 loss_obj: 3931.265137 loss_cls: 21.218021 loss: 3967.662598 eta: 1:48:51 batch_cost: 0.6381 data_cost: 0.0003 ips: 18.8059 images/s\n",
      "[08/13 14:27:34] ppdet.engine INFO: Epoch: [6] [ 0/36] learning_rate: 0.000180 loss_xy: 1.485456 loss_wh: 4.647593 loss_iou: 5.333916 loss_iou_aware: 1.045419 loss_obj: 2366.689941 loss_cls: 20.453833 loss: 2406.093262 eta: 1:46:54 batch_cost: 0.5454 data_cost: 0.0160 ips: 22.0027 images/s\n",
      "[08/13 14:27:50] ppdet.engine INFO: Epoch: [6] [20/36] learning_rate: 0.000196 loss_xy: 1.453399 loss_wh: 4.304494 loss_iou: 5.264187 loss_iou_aware: 1.057942 loss_obj: 2557.855469 loss_cls: 19.214436 loss: 2590.504883 eta: 1:46:16 batch_cost: 0.6438 data_cost: 0.0003 ips: 18.6394 images/s\n",
      "[08/13 14:28:02] ppdet.engine INFO: Epoch: [7] [ 0/36] learning_rate: 0.000210 loss_xy: 1.447591 loss_wh: 4.477379 loss_iou: 5.729889 loss_iou_aware: 1.118932 loss_obj: 1225.561646 loss_cls: 19.991116 loss: 1259.981934 eta: 1:45:25 batch_cost: 0.5853 data_cost: 0.0339 ips: 20.5011 images/s\n",
      "[08/13 14:28:16] ppdet.engine INFO: Epoch: [7] [20/36] learning_rate: 0.000226 loss_xy: 1.399295 loss_wh: 3.570784 loss_iou: 5.001944 loss_iou_aware: 0.912080 loss_obj: 1427.188599 loss_cls: 18.465197 loss: 1452.637207 eta: 1:44:21 batch_cost: 0.5943 data_cost: 0.0003 ips: 20.1915 images/s\n",
      "[08/13 14:28:32] ppdet.engine INFO: Epoch: [8] [ 0/36] learning_rate: 0.000240 loss_xy: 1.516645 loss_wh: 3.885303 loss_iou: 5.228791 loss_iou_aware: 0.968490 loss_obj: 1330.031494 loss_cls: 19.495264 loss: 1374.059082 eta: 1:45:13 batch_cost: 0.7584 data_cost: 0.0243 ips: 15.8229 images/s\n",
      "[08/13 14:28:48] ppdet.engine INFO: Epoch: [8] [20/36] learning_rate: 0.000256 loss_xy: 1.556392 loss_wh: 4.113739 loss_iou: 5.558086 loss_iou_aware: 1.054635 loss_obj: 1014.494263 loss_cls: 20.031219 loss: 1049.682373 eta: 1:44:41 batch_cost: 0.6379 data_cost: 0.0004 ips: 18.8104 images/s\n",
      "[08/13 14:29:03] ppdet.engine INFO: Epoch: [9] [ 0/36] learning_rate: 0.000270 loss_xy: 1.314506 loss_wh: 4.284663 loss_iou: 5.328701 loss_iou_aware: 1.054057 loss_obj: 926.272583 loss_cls: 19.044506 loss: 956.296509 eta: 1:45:28 batch_cost: 0.7446 data_cost: 0.0450 ips: 16.1157 images/s\n",
      "[08/13 14:29:19] ppdet.engine INFO: Epoch: [9] [20/36] learning_rate: 0.000286 loss_xy: 1.426091 loss_wh: 4.146270 loss_iou: 5.583636 loss_iou_aware: 1.029731 loss_obj: 532.697876 loss_cls: 20.455463 loss: 568.641479 eta: 1:45:23 batch_cost: 0.6889 data_cost: 0.0003 ips: 17.4183 images/s\n",
      "[08/13 14:29:35] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:29:36] ppdet.engine INFO: Epoch: [10] [ 0/36] learning_rate: 0.000300 loss_xy: 1.395504 loss_wh: 4.233470 loss_iou: 5.478144 loss_iou_aware: 1.100127 loss_obj: 545.207275 loss_cls: 19.533215 loss: 578.717712 eta: 1:45:17 batch_cost: 0.6836 data_cost: 0.0002 ips: 17.5545 images/s\n",
      "[08/13 14:29:51] ppdet.engine INFO: Epoch: [10] [20/36] learning_rate: 0.000316 loss_xy: 1.463245 loss_wh: 4.088609 loss_iou: 5.366575 loss_iou_aware: 0.906951 loss_obj: 304.074921 loss_cls: 20.359362 loss: 331.693481 eta: 1:44:57 batch_cost: 0.6607 data_cost: 0.0003 ips: 18.1636 images/s\n",
      "[08/13 14:30:12] ppdet.engine INFO: Epoch: [11] [ 0/36] learning_rate: 0.000330 loss_xy: 1.428366 loss_wh: 4.336716 loss_iou: 5.738229 loss_iou_aware: 1.039789 loss_obj: 368.017944 loss_cls: 20.248745 loss: 400.638306 eta: 1:47:44 batch_cost: 1.0309 data_cost: 0.2924 ips: 11.6402 images/s\n",
      "[08/13 14:30:28] ppdet.engine INFO: Epoch: [11] [20/36] learning_rate: 0.000346 loss_xy: 1.677689 loss_wh: 4.434068 loss_iou: 5.866139 loss_iou_aware: 1.116972 loss_obj: 226.978943 loss_cls: 20.959265 loss: 257.454498 eta: 1:47:08 batch_cost: 0.6454 data_cost: 0.0003 ips: 18.5919 images/s\n",
      "[08/13 14:30:42] ppdet.engine INFO: Epoch: [12] [ 0/36] learning_rate: 0.000360 loss_xy: 1.520970 loss_wh: 4.252525 loss_iou: 5.615762 loss_iou_aware: 1.032122 loss_obj: 191.715973 loss_cls: 19.379280 loss: 223.473038 eta: 1:47:05 batch_cost: 0.7090 data_cost: 0.0588 ips: 16.9249 images/s\n",
      "[08/13 14:30:58] ppdet.engine INFO: Epoch: [12] [20/36] learning_rate: 0.000376 loss_xy: 1.418185 loss_wh: 4.126655 loss_iou: 5.398523 loss_iou_aware: 0.966675 loss_obj: 147.442139 loss_cls: 19.459715 loss: 181.867889 eta: 1:46:34 batch_cost: 0.6495 data_cost: 0.0003 ips: 18.4756 images/s\n",
      "[08/13 14:31:10] ppdet.engine INFO: Epoch: [13] [ 0/36] learning_rate: 0.000390 loss_xy: 1.470975 loss_wh: 4.266130 loss_iou: 5.615298 loss_iou_aware: 1.060976 loss_obj: 108.984558 loss_cls: 19.933968 loss: 144.245926 eta: 1:45:34 batch_cost: 0.5332 data_cost: 0.0003 ips: 22.5065 images/s\n",
      "[08/13 14:31:24] ppdet.engine INFO: Epoch: [13] [20/36] learning_rate: 0.000406 loss_xy: 1.291037 loss_wh: 4.126808 loss_iou: 5.524447 loss_iou_aware: 1.038716 loss_obj: 88.278343 loss_cls: 19.260437 loss: 119.711647 eta: 1:44:42 batch_cost: 0.5831 data_cost: 0.0003 ips: 20.5800 images/s\n",
      "[08/13 14:31:37] ppdet.engine INFO: Epoch: [14] [ 0/36] learning_rate: 0.000420 loss_xy: 1.438102 loss_wh: 4.272804 loss_iou: 5.578398 loss_iou_aware: 1.093245 loss_obj: 105.046906 loss_cls: 19.692924 loss: 141.336243 eta: 1:44:29 batch_cost: 0.6352 data_cost: 0.0668 ips: 18.8921 images/s\n",
      "[08/13 14:31:53] ppdet.engine INFO: Epoch: [14] [20/36] learning_rate: 0.000436 loss_xy: 1.484572 loss_wh: 4.214875 loss_iou: 5.641372 loss_iou_aware: 1.055100 loss_obj: 89.590881 loss_cls: 19.204075 loss: 127.944839 eta: 1:44:07 batch_cost: 0.6551 data_cost: 0.0003 ips: 18.3171 images/s\n",
      "[08/13 14:32:06] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:32:08] ppdet.engine INFO: Epoch: [15] [ 0/36] learning_rate: 0.000450 loss_xy: 1.544548 loss_wh: 3.925365 loss_iou: 5.303506 loss_iou_aware: 0.993521 loss_obj: 62.916061 loss_cls: 18.751991 loss: 97.192497 eta: 1:43:32 batch_cost: 0.5930 data_cost: 0.0096 ips: 20.2368 images/s\n",
      "[08/13 14:32:22] ppdet.engine INFO: Epoch: [15] [20/36] learning_rate: 0.000466 loss_xy: 1.529171 loss_wh: 3.901572 loss_iou: 5.507113 loss_iou_aware: 1.042820 loss_obj: 68.440331 loss_cls: 19.879108 loss: 107.664238 eta: 1:43:00 batch_cost: 0.6219 data_cost: 0.0003 ips: 19.2946 images/s\n",
      "[08/13 14:32:36] ppdet.engine INFO: Epoch: [16] [ 0/36] learning_rate: 0.000480 loss_xy: 1.407513 loss_wh: 3.839614 loss_iou: 5.092416 loss_iou_aware: 0.980410 loss_obj: 50.317307 loss_cls: 18.164524 loss: 78.044403 eta: 1:42:45 batch_cost: 0.6294 data_cost: 0.0142 ips: 19.0654 images/s\n",
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      "[08/13 14:33:06] ppdet.engine INFO: Epoch: [17] [ 0/36] learning_rate: 0.000509 loss_xy: 1.521995 loss_wh: 4.027006 loss_iou: 5.684019 loss_iou_aware: 1.020652 loss_obj: 56.574089 loss_cls: 19.905727 loss: 86.922249 eta: 1:42:12 batch_cost: 0.6853 data_cost: 0.0434 ips: 17.5118 images/s\n",
      "[08/13 14:33:23] ppdet.engine INFO: Epoch: [17] [20/36] learning_rate: 0.000526 loss_xy: 1.391986 loss_wh: 3.217383 loss_iou: 4.985141 loss_iou_aware: 0.905046 loss_obj: 56.607895 loss_cls: 17.629959 loss: 85.174454 eta: 1:42:25 batch_cost: 0.7651 data_cost: 0.0003 ips: 15.6846 images/s\n",
      "[08/13 14:33:36] ppdet.engine INFO: Epoch: [18] [ 0/36] learning_rate: 0.000539 loss_xy: 1.543482 loss_wh: 4.046550 loss_iou: 5.489156 loss_iou_aware: 1.026340 loss_obj: 43.339115 loss_cls: 19.025883 loss: 78.882065 eta: 1:41:51 batch_cost: 0.5698 data_cost: 0.0076 ips: 21.0586 images/s\n",
      "[08/13 14:33:52] ppdet.engine INFO: Epoch: [18] [20/36] learning_rate: 0.000556 loss_xy: 1.508641 loss_wh: 3.820807 loss_iou: 5.204885 loss_iou_aware: 1.039531 loss_obj: 40.142761 loss_cls: 18.762741 loss: 74.254608 eta: 1:41:40 batch_cost: 0.6801 data_cost: 0.0356 ips: 17.6445 images/s\n",
      "[08/13 14:34:06] ppdet.engine INFO: Epoch: [19] [ 0/36] learning_rate: 0.000569 loss_xy: 1.341634 loss_wh: 3.780266 loss_iou: 5.056756 loss_iou_aware: 0.953339 loss_obj: 36.549248 loss_cls: 17.882004 loss: 68.234932 eta: 1:41:31 batch_cost: 0.6517 data_cost: 0.0023 ips: 18.4137 images/s\n",
      "[08/13 14:34:21] ppdet.engine INFO: Epoch: [19] [20/36] learning_rate: 0.000586 loss_xy: 1.438256 loss_wh: 4.023202 loss_iou: 5.266199 loss_iou_aware: 1.052536 loss_obj: 34.720184 loss_cls: 19.028019 loss: 68.280037 eta: 1:41:07 batch_cost: 0.6330 data_cost: 0.0002 ips: 18.9571 images/s\n",
      "[08/13 14:34:34] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:34:35] ppdet.engine INFO: Epoch: [20] [ 0/36] learning_rate: 0.000599 loss_xy: 1.458726 loss_wh: 3.762443 loss_iou: 5.430209 loss_iou_aware: 1.088290 loss_obj: 33.939510 loss_cls: 18.093018 loss: 63.713234 eta: 1:40:37 batch_cost: 0.6350 data_cost: 0.0002 ips: 18.8972 images/s\n",
      "[08/13 14:34:50] ppdet.engine INFO: Epoch: [20] [20/36] learning_rate: 0.000616 loss_xy: 1.619778 loss_wh: 3.897979 loss_iou: 5.807170 loss_iou_aware: 1.060135 loss_obj: 30.616377 loss_cls: 19.511261 loss: 65.135605 eta: 1:40:13 batch_cost: 0.6244 data_cost: 0.0002 ips: 19.2172 images/s\n",
      "[08/13 14:35:05] ppdet.engine INFO: Epoch: [21] [ 0/36] learning_rate: 0.000629 loss_xy: 1.374655 loss_wh: 3.876762 loss_iou: 5.581394 loss_iou_aware: 1.018033 loss_obj: 30.984571 loss_cls: 19.193827 loss: 64.426727 eta: 1:40:21 batch_cost: 0.7728 data_cost: 0.0652 ips: 15.5271 images/s\n",
      "[08/13 14:35:19] ppdet.engine INFO: Epoch: [21] [20/36] learning_rate: 0.000646 loss_xy: 1.372567 loss_wh: 3.785298 loss_iou: 5.105392 loss_iou_aware: 1.000383 loss_obj: 25.186274 loss_cls: 17.823772 loss: 59.427803 eta: 1:39:45 batch_cost: 0.5738 data_cost: 0.0003 ips: 20.9141 images/s\n",
      "[08/13 14:35:34] ppdet.engine INFO: Epoch: [22] [ 0/36] learning_rate: 0.000659 loss_xy: 1.433139 loss_wh: 3.553906 loss_iou: 5.032535 loss_iou_aware: 1.000585 loss_obj: 24.325966 loss_cls: 17.825014 loss: 56.199368 eta: 1:39:48 batch_cost: 0.7091 data_cost: 0.0718 ips: 16.9239 images/s\n",
      "[08/13 14:35:49] ppdet.engine INFO: Epoch: [22] [20/36] learning_rate: 0.000676 loss_xy: 1.409462 loss_wh: 3.751305 loss_iou: 5.036649 loss_iou_aware: 0.913098 loss_obj: 25.528915 loss_cls: 16.944469 loss: 54.714645 eta: 1:39:33 batch_cost: 0.6618 data_cost: 0.0002 ips: 18.1337 images/s\n",
      "[08/13 14:36:02] ppdet.engine INFO: Epoch: [23] [ 0/36] learning_rate: 0.000689 loss_xy: 1.430123 loss_wh: 3.917843 loss_iou: 5.456603 loss_iou_aware: 1.017806 loss_obj: 22.211592 loss_cls: 18.520830 loss: 51.524094 eta: 1:39:04 batch_cost: 0.5794 data_cost: 0.0065 ips: 20.7106 images/s\n",
      "[08/13 14:36:18] ppdet.engine INFO: Epoch: [23] [20/36] learning_rate: 0.000706 loss_xy: 1.593859 loss_wh: 3.745122 loss_iou: 5.851978 loss_iou_aware: 1.040529 loss_obj: 26.871077 loss_cls: 19.326954 loss: 57.989510 eta: 1:38:59 batch_cost: 0.7076 data_cost: 0.0003 ips: 16.9599 images/s\n",
      "[08/13 14:36:30] ppdet.engine INFO: Epoch: [24] [ 0/36] learning_rate: 0.000719 loss_xy: 1.404897 loss_wh: 3.705170 loss_iou: 5.406056 loss_iou_aware: 1.021067 loss_obj: 18.883854 loss_cls: 18.914310 loss: 53.925438 eta: 1:38:26 batch_cost: 0.5834 data_cost: 0.0002 ips: 20.5679 images/s\n",
      "[08/13 14:36:46] ppdet.engine INFO: Epoch: [24] [20/36] learning_rate: 0.000736 loss_xy: 1.441153 loss_wh: 3.765557 loss_iou: 5.506142 loss_iou_aware: 1.049779 loss_obj: 20.244251 loss_cls: 18.324902 loss: 52.651657 eta: 1:38:16 batch_cost: 0.6823 data_cost: 0.0003 ips: 17.5883 images/s\n",
      "[08/13 14:37:00] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:37:00] ppdet.engine INFO: Epoch: [25] [ 0/36] learning_rate: 0.000749 loss_xy: 1.422340 loss_wh: 3.642916 loss_iou: 4.904286 loss_iou_aware: 0.959472 loss_obj: 20.443905 loss_cls: 16.230679 loss: 47.922718 eta: 1:37:56 batch_cost: 0.6091 data_cost: 0.0002 ips: 19.7026 images/s\n",
      "[08/13 14:37:15] ppdet.engine INFO: Epoch: [25] [20/36] learning_rate: 0.000766 loss_xy: 1.484704 loss_wh: 4.178996 loss_iou: 5.802648 loss_iou_aware: 1.087662 loss_obj: 18.214127 loss_cls: 19.315195 loss: 50.830952 eta: 1:37:34 batch_cost: 0.6240 data_cost: 0.0002 ips: 19.2315 images/s\n",
      "[08/13 14:37:29] ppdet.engine INFO: Epoch: [26] [ 0/36] learning_rate: 0.000779 loss_xy: 1.458172 loss_wh: 4.471339 loss_iou: 6.020663 loss_iou_aware: 1.130918 loss_obj: 22.362640 loss_cls: 20.214985 loss: 51.320950 eta: 1:37:33 batch_cost: 0.7235 data_cost: 0.0170 ips: 16.5862 images/s\n",
      "[08/13 14:37:42] ppdet.engine INFO: Epoch: [26] [20/36] learning_rate: 0.000796 loss_xy: 1.382439 loss_wh: 3.365225 loss_iou: 5.051147 loss_iou_aware: 0.929122 loss_obj: 15.942299 loss_cls: 16.421492 loss: 44.202389 eta: 1:36:55 batch_cost: 0.5314 data_cost: 0.0002 ips: 22.5836 images/s\n",
      "[08/13 14:37:57] ppdet.engine INFO: Epoch: [27] [ 0/36] learning_rate: 0.000809 loss_xy: 1.541625 loss_wh: 4.141446 loss_iou: 5.604163 loss_iou_aware: 1.108230 loss_obj: 18.536438 loss_cls: 18.012539 loss: 48.123169 eta: 1:36:54 batch_cost: 0.7134 data_cost: 0.0341 ips: 16.8220 images/s\n",
      "[08/13 14:38:13] ppdet.engine INFO: Epoch: [27] [20/36] learning_rate: 0.000826 loss_xy: 1.486576 loss_wh: 3.743483 loss_iou: 5.567853 loss_iou_aware: 1.037501 loss_obj: 17.809340 loss_cls: 17.246759 loss: 46.714828 eta: 1:36:39 batch_cost: 0.6547 data_cost: 0.0003 ips: 18.3300 images/s\n",
      "[08/13 14:38:30] ppdet.engine INFO: Epoch: [28] [ 0/36] learning_rate: 0.000839 loss_xy: 1.416735 loss_wh: 3.372646 loss_iou: 5.290932 loss_iou_aware: 0.979242 loss_obj: 16.951164 loss_cls: 16.744375 loss: 44.089565 eta: 1:36:36 batch_cost: 0.7342 data_cost: 0.0003 ips: 16.3442 images/s\n",
      "[08/13 14:38:46] ppdet.engine INFO: Epoch: [28] [20/36] learning_rate: 0.000856 loss_xy: 1.590394 loss_wh: 4.063664 loss_iou: 5.757080 loss_iou_aware: 1.075463 loss_obj: 18.160679 loss_cls: 17.937416 loss: 46.853096 eta: 1:36:22 batch_cost: 0.6616 data_cost: 0.0042 ips: 18.1374 images/s\n",
      "[08/13 14:38:59] ppdet.engine INFO: Epoch: [29] [ 0/36] learning_rate: 0.000869 loss_xy: 1.468055 loss_wh: 3.592992 loss_iou: 5.252556 loss_iou_aware: 0.982570 loss_obj: 14.495787 loss_cls: 15.669550 loss: 41.882828 eta: 1:36:09 batch_cost: 0.6254 data_cost: 0.0616 ips: 19.1868 images/s\n",
      "[08/13 14:39:15] ppdet.engine INFO: Epoch: [29] [20/36] learning_rate: 0.000886 loss_xy: 1.308701 loss_wh: 3.167506 loss_iou: 5.112778 loss_iou_aware: 0.936178 loss_obj: 15.383249 loss_cls: 14.503143 loss: 39.907421 eta: 1:35:54 batch_cost: 0.6508 data_cost: 0.0003 ips: 18.4384 images/s\n",
      "[08/13 14:39:31] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:39:31] ppdet.engine INFO: Epoch: [30] [ 0/36] learning_rate: 0.000899 loss_xy: 1.285014 loss_wh: 3.199710 loss_iou: 5.112778 loss_iou_aware: 0.956163 loss_obj: 15.356161 loss_cls: 14.714533 loss: 39.850220 eta: 1:35:49 batch_cost: 0.6801 data_cost: 0.0002 ips: 17.6437 images/s\n",
      "[08/13 14:39:49] ppdet.engine INFO: Epoch: [30] [20/36] learning_rate: 0.000916 loss_xy: 1.392828 loss_wh: 3.289876 loss_iou: 4.885717 loss_iou_aware: 0.915819 loss_obj: 15.431724 loss_cls: 14.004512 loss: 38.745914 eta: 1:35:47 batch_cost: 0.7332 data_cost: 0.0003 ips: 16.3657 images/s\n",
      "[08/13 14:40:04] ppdet.engine INFO: Epoch: [31] [ 0/36] learning_rate: 0.000929 loss_xy: 1.370878 loss_wh: 3.440532 loss_iou: 5.425562 loss_iou_aware: 0.972591 loss_obj: 14.939195 loss_cls: 14.112196 loss: 40.741402 eta: 1:35:43 batch_cost: 0.7313 data_cost: 0.0191 ips: 16.4090 images/s\n",
      "[08/13 14:40:20] ppdet.engine INFO: Epoch: [31] [20/36] learning_rate: 0.000946 loss_xy: 1.423511 loss_wh: 3.372391 loss_iou: 5.252077 loss_iou_aware: 0.990929 loss_obj: 13.475918 loss_cls: 12.781487 loss: 37.531189 eta: 1:35:33 batch_cost: 0.6903 data_cost: 0.0002 ips: 17.3827 images/s\n",
      "[08/13 14:40:33] ppdet.engine INFO: Epoch: [32] [ 0/36] learning_rate: 0.000959 loss_xy: 1.352053 loss_wh: 2.887351 loss_iou: 5.133989 loss_iou_aware: 0.967476 loss_obj: 12.934477 loss_cls: 10.727589 loss: 35.230141 eta: 1:35:19 batch_cost: 0.6664 data_cost: 0.0075 ips: 18.0076 images/s\n",
      "[08/13 14:40:49] ppdet.engine INFO: Epoch: [32] [20/36] learning_rate: 0.000976 loss_xy: 1.445300 loss_wh: 3.006922 loss_iou: 5.339454 loss_iou_aware: 0.957950 loss_obj: 13.687097 loss_cls: 10.301590 loss: 35.287739 eta: 1:35:02 batch_cost: 0.6453 data_cost: 0.0002 ips: 18.5955 images/s\n",
      "[08/13 14:41:01] ppdet.engine INFO: Epoch: [33] [ 0/36] learning_rate: 0.000989 loss_xy: 1.382339 loss_wh: 2.823089 loss_iou: 5.191601 loss_iou_aware: 0.981478 loss_obj: 12.109509 loss_cls: 9.734534 loss: 32.378189 eta: 1:34:42 batch_cost: 0.5940 data_cost: 0.0002 ips: 20.2009 images/s\n",
      "[08/13 14:41:18] ppdet.engine INFO: Epoch: [33] [20/36] learning_rate: 0.001006 loss_xy: 1.326865 loss_wh: 2.780962 loss_iou: 5.186435 loss_iou_aware: 0.958312 loss_obj: 11.914454 loss_cls: 8.250234 loss: 30.868698 eta: 1:34:29 batch_cost: 0.6683 data_cost: 0.0003 ips: 17.9562 images/s\n",
      "[08/13 14:41:32] ppdet.engine INFO: Epoch: [34] [ 0/36] learning_rate: 0.001019 loss_xy: 1.343734 loss_wh: 2.586752 loss_iou: 4.951044 loss_iou_aware: 0.989011 loss_obj: 11.956121 loss_cls: 7.736709 loss: 29.134407 eta: 1:34:22 batch_cost: 0.7215 data_cost: 0.0545 ips: 16.6332 images/s\n",
      "[08/13 14:41:50] ppdet.engine INFO: Epoch: [34] [20/36] learning_rate: 0.001036 loss_xy: 1.252294 loss_wh: 2.233864 loss_iou: 4.476492 loss_iou_aware: 0.855613 loss_obj: 11.244103 loss_cls: 5.426216 loss: 24.710672 eta: 1:34:23 batch_cost: 0.7730 data_cost: 0.0357 ips: 15.5240 images/s\n",
      "[08/13 14:42:03] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:42:04] ppdet.engine INFO: Epoch: [35] [ 0/36] learning_rate: 0.001049 loss_xy: 1.234095 loss_wh: 2.451406 loss_iou: 4.503110 loss_iou_aware: 0.871931 loss_obj: 10.224923 loss_cls: 4.843244 loss: 24.197605 eta: 1:33:59 batch_cost: 0.5854 data_cost: 0.0003 ips: 20.5004 images/s\n",
      "[08/13 14:42:18] ppdet.engine INFO: Epoch: [35] [20/36] learning_rate: 0.001066 loss_xy: 1.282637 loss_wh: 2.475688 loss_iou: 5.007102 loss_iou_aware: 0.865249 loss_obj: 10.209236 loss_cls: 3.692954 loss: 24.103846 eta: 1:33:39 batch_cost: 0.6133 data_cost: 0.0003 ips: 19.5671 images/s\n",
      "[08/13 14:42:34] ppdet.engine INFO: Epoch: [36] [ 0/36] learning_rate: 0.001079 loss_xy: 1.258082 loss_wh: 2.504434 loss_iou: 4.872124 loss_iou_aware: 0.870350 loss_obj: 10.706377 loss_cls: 3.169633 loss: 23.130917 eta: 1:33:40 batch_cost: 0.7804 data_cost: 0.0003 ips: 15.3762 images/s\n",
      "[08/13 14:42:50] ppdet.engine INFO: Epoch: [36] [20/36] learning_rate: 0.001096 loss_xy: 1.257359 loss_wh: 2.639505 loss_iou: 4.851962 loss_iou_aware: 0.858816 loss_obj: 10.105160 loss_cls: 2.651959 loss: 22.740082 eta: 1:33:25 batch_cost: 0.6514 data_cost: 0.0003 ips: 18.4216 images/s\n",
      "[08/13 14:43:04] ppdet.engine INFO: Epoch: [37] [ 0/36] learning_rate: 0.001109 loss_xy: 1.139628 loss_wh: 2.153350 loss_iou: 4.383039 loss_iou_aware: 0.841556 loss_obj: 10.090399 loss_cls: 2.089969 loss: 20.664797 eta: 1:33:10 batch_cost: 0.6481 data_cost: 0.0003 ips: 18.5163 images/s\n",
      "[08/13 14:43:21] ppdet.engine INFO: Epoch: [37] [20/36] learning_rate: 0.001126 loss_xy: 1.143022 loss_wh: 2.196546 loss_iou: 4.394794 loss_iou_aware: 0.802806 loss_obj: 9.666180 loss_cls: 1.861268 loss: 20.260925 eta: 1:33:07 batch_cost: 0.7474 data_cost: 0.0268 ips: 16.0559 images/s\n",
      "[08/13 14:43:34] ppdet.engine INFO: Epoch: [38] [ 0/36] learning_rate: 0.001139 loss_xy: 1.148536 loss_wh: 2.228459 loss_iou: 4.602114 loss_iou_aware: 0.805064 loss_obj: 9.114012 loss_cls: 1.647132 loss: 19.873457 eta: 1:32:52 batch_cost: 0.6131 data_cost: 0.0002 ips: 19.5728 images/s\n",
      "[08/13 14:43:49] ppdet.engine INFO: Epoch: [38] [20/36] learning_rate: 0.001156 loss_xy: 1.188992 loss_wh: 2.357666 loss_iou: 4.846408 loss_iou_aware: 0.868019 loss_obj: 9.854721 loss_cls: 1.866235 loss: 20.878960 eta: 1:32:31 batch_cost: 0.6084 data_cost: 0.0003 ips: 19.7229 images/s\n",
      "[08/13 14:44:02] ppdet.engine INFO: Epoch: [39] [ 0/36] learning_rate: 0.001169 loss_xy: 1.260797 loss_wh: 2.278406 loss_iou: 5.002093 loss_iou_aware: 0.907625 loss_obj: 9.603113 loss_cls: 1.578789 loss: 20.691856 eta: 1:32:21 batch_cost: 0.6467 data_cost: 0.0354 ips: 18.5558 images/s\n",
      "[08/13 14:44:18] ppdet.engine INFO: Epoch: [39] [20/36] learning_rate: 0.001185 loss_xy: 1.290156 loss_wh: 2.330468 loss_iou: 4.952043 loss_iou_aware: 0.866204 loss_obj: 9.160330 loss_cls: 1.545745 loss: 19.648510 eta: 1:32:07 batch_cost: 0.6549 data_cost: 0.0003 ips: 18.3237 images/s\n",
      "[08/13 14:44:32] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:44:34] ppdet.engine INFO: Epoch: [40] [ 0/36] learning_rate: 0.001199 loss_xy: 1.174059 loss_wh: 2.483067 loss_iou: 4.493376 loss_iou_aware: 0.840450 loss_obj: 9.164802 loss_cls: 1.242803 loss: 19.438461 eta: 1:31:54 batch_cost: 0.6642 data_cost: 0.0002 ips: 18.0662 images/s\n",
      "[08/13 14:44:49] ppdet.engine INFO: Epoch: [40] [20/36] learning_rate: 0.001215 loss_xy: 1.131963 loss_wh: 2.063375 loss_iou: 4.288192 loss_iou_aware: 0.847201 loss_obj: 8.544773 loss_cls: 1.203327 loss: 18.070366 eta: 1:31:40 batch_cost: 0.6632 data_cost: 0.0003 ips: 18.0952 images/s\n",
      "[08/13 14:45:04] ppdet.engine INFO: Epoch: [41] [ 0/36] learning_rate: 0.001229 loss_xy: 1.201815 loss_wh: 2.004344 loss_iou: 4.425320 loss_iou_aware: 0.827549 loss_obj: 9.113544 loss_cls: 1.112736 loss: 19.317188 eta: 1:31:35 batch_cost: 0.7003 data_cost: 0.0391 ips: 17.1358 images/s\n",
      "[08/13 14:45:19] ppdet.engine INFO: Epoch: [41] [20/36] learning_rate: 0.001245 loss_xy: 1.183066 loss_wh: 2.194548 loss_iou: 4.598028 loss_iou_aware: 0.869308 loss_obj: 8.926434 loss_cls: 0.857466 loss: 18.877293 eta: 1:31:18 batch_cost: 0.6267 data_cost: 0.0003 ips: 19.1479 images/s\n",
      "[08/13 14:45:32] ppdet.engine INFO: Epoch: [42] [ 0/36] learning_rate: 0.001259 loss_xy: 1.148990 loss_wh: 2.122565 loss_iou: 4.608188 loss_iou_aware: 0.857496 loss_obj: 9.106712 loss_cls: 0.842765 loss: 18.788868 eta: 1:31:08 batch_cost: 0.6409 data_cost: 0.0145 ips: 18.7233 images/s\n",
      "[08/13 14:45:48] ppdet.engine INFO: Epoch: [42] [20/36] learning_rate: 0.001275 loss_xy: 1.173412 loss_wh: 2.049834 loss_iou: 4.468445 loss_iou_aware: 0.874828 loss_obj: 8.620848 loss_cls: 0.650402 loss: 17.995205 eta: 1:30:56 batch_cost: 0.6859 data_cost: 0.0003 ips: 17.4948 images/s\n",
      "[08/13 14:46:01] ppdet.engine INFO: Epoch: [43] [ 0/36] learning_rate: 0.001289 loss_xy: 1.264367 loss_wh: 2.167143 loss_iou: 4.636416 loss_iou_aware: 0.813775 loss_obj: 8.046817 loss_cls: 0.649300 loss: 17.664974 eta: 1:30:39 batch_cost: 0.5917 data_cost: 0.0003 ips: 20.2793 images/s\n",
      "[08/13 14:46:16] ppdet.engine INFO: Epoch: [43] [20/36] learning_rate: 0.001305 loss_xy: 1.202300 loss_wh: 2.158911 loss_iou: 4.688986 loss_iou_aware: 0.908752 loss_obj: 8.799802 loss_cls: 0.795316 loss: 19.112381 eta: 1:30:25 batch_cost: 0.6635 data_cost: 0.0003 ips: 18.0859 images/s\n",
      "[08/13 14:46:31] ppdet.engine INFO: Epoch: [44] [ 0/36] learning_rate: 0.001319 loss_xy: 1.081482 loss_wh: 2.294484 loss_iou: 4.582500 loss_iou_aware: 0.832030 loss_obj: 8.698306 loss_cls: 0.659210 loss: 18.119648 eta: 1:30:18 batch_cost: 0.7121 data_cost: 0.0270 ips: 16.8515 images/s\n",
      "[08/13 14:46:48] ppdet.engine INFO: Epoch: [44] [20/36] learning_rate: 0.001335 loss_xy: 1.077614 loss_wh: 2.272002 loss_iou: 4.626211 loss_iou_aware: 0.879281 loss_obj: 8.598360 loss_cls: 0.623924 loss: 18.740604 eta: 1:30:14 batch_cost: 0.7617 data_cost: 0.0003 ips: 15.7537 images/s\n",
      "[08/13 14:47:00] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:47:01] ppdet.engine INFO: Epoch: [45] [ 0/36] learning_rate: 0.001349 loss_xy: 1.145384 loss_wh: 2.494906 loss_iou: 4.940865 loss_iou_aware: 0.894444 loss_obj: 8.648545 loss_cls: 0.676479 loss: 19.511450 eta: 1:29:51 batch_cost: 0.5608 data_cost: 0.0029 ips: 21.3975 images/s\n",
      "[08/13 14:47:16] ppdet.engine INFO: Epoch: [45] [20/36] learning_rate: 0.001365 loss_xy: 1.062597 loss_wh: 1.821828 loss_iou: 4.168540 loss_iou_aware: 0.777751 loss_obj: 8.446891 loss_cls: 0.657547 loss: 16.215427 eta: 1:29:30 batch_cost: 0.5833 data_cost: 0.0003 ips: 20.5722 images/s\n",
      "[08/13 14:47:29] ppdet.engine INFO: Epoch: [46] [ 0/36] learning_rate: 0.001379 loss_xy: 1.121914 loss_wh: 1.979945 loss_iou: 4.709577 loss_iou_aware: 0.870814 loss_obj: 8.526342 loss_cls: 0.565280 loss: 18.726254 eta: 1:29:18 batch_cost: 0.6320 data_cost: 0.0107 ips: 18.9885 images/s\n",
      "[08/13 14:47:44] ppdet.engine INFO: Epoch: [46] [20/36] learning_rate: 0.001395 loss_xy: 1.086714 loss_wh: 2.080246 loss_iou: 4.518252 loss_iou_aware: 0.826358 loss_obj: 7.591548 loss_cls: 0.506217 loss: 16.735907 eta: 1:29:02 batch_cost: 0.6333 data_cost: 0.0003 ips: 18.9479 images/s\n",
      "[08/13 14:47:58] ppdet.engine INFO: Epoch: [47] [ 0/36] learning_rate: 0.001409 loss_xy: 1.158111 loss_wh: 1.947370 loss_iou: 4.617585 loss_iou_aware: 0.875341 loss_obj: 8.492687 loss_cls: 0.510258 loss: 18.043686 eta: 1:28:55 batch_cost: 0.6799 data_cost: 0.0003 ips: 17.6500 images/s\n",
      "[08/13 14:48:14] ppdet.engine INFO: Epoch: [47] [20/36] learning_rate: 0.001425 loss_xy: 1.110418 loss_wh: 1.953967 loss_iou: 4.418067 loss_iou_aware: 0.882003 loss_obj: 7.825019 loss_cls: 0.541728 loss: 16.608435 eta: 1:28:39 batch_cost: 0.6367 data_cost: 0.0003 ips: 18.8473 images/s\n",
      "[08/13 14:48:26] ppdet.engine INFO: Epoch: [48] [ 0/36] learning_rate: 0.001439 loss_xy: 1.119707 loss_wh: 2.380600 loss_iou: 4.504179 loss_iou_aware: 0.829156 loss_obj: 7.843585 loss_cls: 0.589782 loss: 16.778881 eta: 1:28:26 batch_cost: 0.6325 data_cost: 0.0003 ips: 18.9727 images/s\n",
      "[08/13 14:48:41] ppdet.engine INFO: Epoch: [48] [20/36] learning_rate: 0.001455 loss_xy: 1.030747 loss_wh: 2.225339 loss_iou: 4.416183 loss_iou_aware: 0.871443 loss_obj: 7.735165 loss_cls: 0.556657 loss: 16.692635 eta: 1:28:10 batch_cost: 0.6360 data_cost: 0.0003 ips: 18.8670 images/s\n",
      "[08/13 14:48:56] ppdet.engine INFO: Epoch: [49] [ 0/36] learning_rate: 0.001469 loss_xy: 1.005410 loss_wh: 2.050156 loss_iou: 4.475846 loss_iou_aware: 0.881588 loss_obj: 7.395243 loss_cls: 0.428566 loss: 16.222815 eta: 1:28:03 batch_cost: 0.6807 data_cost: 0.0415 ips: 17.6288 images/s\n",
      "[08/13 14:49:13] ppdet.engine INFO: Epoch: [49] [20/36] learning_rate: 0.001485 loss_xy: 1.070089 loss_wh: 1.924179 loss_iou: 4.259029 loss_iou_aware: 0.852681 loss_obj: 8.188206 loss_cls: 0.426719 loss: 16.571564 eta: 1:27:56 batch_cost: 0.7324 data_cost: 0.0002 ips: 16.3840 images/s\n",
      "[08/13 14:49:26] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:49:27] ppdet.engine INFO: Epoch: [50] [ 0/36] learning_rate: 0.001499 loss_xy: 1.040577 loss_wh: 1.714096 loss_iou: 4.289660 loss_iou_aware: 0.843389 loss_obj: 7.902801 loss_cls: 0.392273 loss: 15.961956 eta: 1:27:38 batch_cost: 0.5731 data_cost: 0.0002 ips: 20.9370 images/s\n",
      "[08/13 14:49:42] ppdet.engine INFO: Epoch: [50] [20/36] learning_rate: 0.001515 loss_xy: 1.077253 loss_wh: 2.001544 loss_iou: 4.510395 loss_iou_aware: 0.858648 loss_obj: 7.595239 loss_cls: 0.429199 loss: 16.582619 eta: 1:27:24 batch_cost: 0.6591 data_cost: 0.0002 ips: 18.2067 images/s\n",
      "[08/13 14:49:53] ppdet.engine INFO: Epoch: [51] [ 0/36] learning_rate: 0.001528 loss_xy: 1.084472 loss_wh: 1.827670 loss_iou: 4.125926 loss_iou_aware: 0.831358 loss_obj: 7.429021 loss_cls: 0.432688 loss: 15.628296 eta: 1:27:03 batch_cost: 0.5472 data_cost: 0.0116 ips: 21.9281 images/s\n",
      "[08/13 14:50:10] ppdet.engine INFO: Epoch: [51] [20/36] learning_rate: 0.001545 loss_xy: 1.110677 loss_wh: 1.885140 loss_iou: 4.477991 loss_iou_aware: 0.853154 loss_obj: 7.080414 loss_cls: 0.376293 loss: 16.259966 eta: 1:26:52 batch_cost: 0.6863 data_cost: 0.0003 ips: 17.4841 images/s\n",
      "[08/13 14:50:24] ppdet.engine INFO: Epoch: [52] [ 0/36] learning_rate: 0.001558 loss_xy: 1.103619 loss_wh: 2.132465 loss_iou: 4.763564 loss_iou_aware: 0.884750 loss_obj: 7.603251 loss_cls: 0.435045 loss: 17.084587 eta: 1:26:43 batch_cost: 0.6830 data_cost: 0.0675 ips: 17.5689 images/s\n",
      "[08/13 14:50:38] ppdet.engine INFO: Epoch: [52] [20/36] learning_rate: 0.001575 loss_xy: 1.043987 loss_wh: 2.051003 loss_iou: 4.551083 loss_iou_aware: 0.845954 loss_obj: 7.208285 loss_cls: 0.429514 loss: 16.000738 eta: 1:26:27 batch_cost: 0.6298 data_cost: 0.0003 ips: 19.0545 images/s\n",
      "[08/13 14:50:51] ppdet.engine INFO: Epoch: [53] [ 0/36] learning_rate: 0.001588 loss_xy: 1.119098 loss_wh: 1.842941 loss_iou: 4.510238 loss_iou_aware: 0.879295 loss_obj: 7.591864 loss_cls: 0.444486 loss: 16.704277 eta: 1:26:14 batch_cost: 0.6413 data_cost: 0.0172 ips: 18.7127 images/s\n",
      "[08/13 14:51:06] ppdet.engine INFO: Epoch: [53] [20/36] learning_rate: 0.001605 loss_xy: 0.946665 loss_wh: 2.065198 loss_iou: 4.160151 loss_iou_aware: 0.832587 loss_obj: 7.356415 loss_cls: 0.400881 loss: 15.391197 eta: 1:25:58 batch_cost: 0.6258 data_cost: 0.0003 ips: 19.1764 images/s\n",
      "[08/13 14:51:19] ppdet.engine INFO: Epoch: [54] [ 0/36] learning_rate: 0.001618 loss_xy: 1.025816 loss_wh: 2.173353 loss_iou: 4.674226 loss_iou_aware: 0.885771 loss_obj: 7.502473 loss_cls: 0.435799 loss: 16.027546 eta: 1:25:46 batch_cost: 0.6184 data_cost: 0.0182 ips: 19.4036 images/s\n",
      "[08/13 14:51:37] ppdet.engine INFO: Epoch: [54] [20/36] learning_rate: 0.001635 loss_xy: 1.003742 loss_wh: 2.221980 loss_iou: 4.695298 loss_iou_aware: 0.880729 loss_obj: 8.117728 loss_cls: 0.449001 loss: 17.031628 eta: 1:25:38 batch_cost: 0.7202 data_cost: 0.0003 ips: 16.6610 images/s\n",
      "[08/13 14:51:53] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:51:53] ppdet.engine INFO: Epoch: [55] [ 0/36] learning_rate: 0.001648 loss_xy: 1.051392 loss_wh: 1.936205 loss_iou: 4.309546 loss_iou_aware: 0.858815 loss_obj: 7.568253 loss_cls: 0.474309 loss: 16.171196 eta: 1:25:30 batch_cost: 0.7079 data_cost: 0.0003 ips: 16.9520 images/s\n",
      "[08/13 14:52:10] ppdet.engine INFO: Epoch: [55] [20/36] learning_rate: 0.001665 loss_xy: 1.119262 loss_wh: 1.831497 loss_iou: 4.457920 loss_iou_aware: 0.917507 loss_obj: 7.823143 loss_cls: 0.402975 loss: 16.524284 eta: 1:25:22 batch_cost: 0.7337 data_cost: 0.0003 ips: 16.3562 images/s\n",
      "[08/13 14:52:25] ppdet.engine INFO: Epoch: [56] [ 0/36] learning_rate: 0.001678 loss_xy: 1.042723 loss_wh: 1.818631 loss_iou: 4.375275 loss_iou_aware: 0.874358 loss_obj: 7.587886 loss_cls: 0.344566 loss: 16.225506 eta: 1:25:17 batch_cost: 0.7327 data_cost: 0.0793 ips: 16.3768 images/s\n",
      "[08/13 14:52:41] ppdet.engine INFO: Epoch: [56] [20/36] learning_rate: 0.001695 loss_xy: 0.930732 loss_wh: 1.954648 loss_iou: 4.177089 loss_iou_aware: 0.805287 loss_obj: 6.562436 loss_cls: 0.347829 loss: 14.512527 eta: 1:25:01 batch_cost: 0.6248 data_cost: 0.0003 ips: 19.2054 images/s\n",
      "[08/13 14:52:55] ppdet.engine INFO: Epoch: [57] [ 0/36] learning_rate: 0.001708 loss_xy: 1.064966 loss_wh: 1.859786 loss_iou: 4.380198 loss_iou_aware: 0.911589 loss_obj: 7.203648 loss_cls: 0.381074 loss: 16.100311 eta: 1:24:55 batch_cost: 0.7758 data_cost: 0.0518 ips: 15.4670 images/s\n",
      "[08/13 14:53:11] ppdet.engine INFO: Epoch: [57] [20/36] learning_rate: 0.001725 loss_xy: 1.040924 loss_wh: 1.866847 loss_iou: 4.065579 loss_iou_aware: 0.817303 loss_obj: 7.207488 loss_cls: 0.313474 loss: 14.941326 eta: 1:24:42 batch_cost: 0.6685 data_cost: 0.0002 ips: 17.9509 images/s\n",
      "[08/13 14:53:24] ppdet.engine INFO: Epoch: [58] [ 0/36] learning_rate: 0.001738 loss_xy: 1.064484 loss_wh: 1.918152 loss_iou: 4.473022 loss_iou_aware: 0.895221 loss_obj: 7.845782 loss_cls: 0.362338 loss: 16.806698 eta: 1:24:31 batch_cost: 0.6613 data_cost: 0.0002 ips: 18.1452 images/s\n",
      "[08/13 14:53:39] ppdet.engine INFO: Epoch: [58] [20/36] learning_rate: 0.001755 loss_xy: 1.004852 loss_wh: 1.691889 loss_iou: 4.446661 loss_iou_aware: 0.894369 loss_obj: 6.872901 loss_cls: 0.339203 loss: 15.146663 eta: 1:24:17 batch_cost: 0.6510 data_cost: 0.0002 ips: 18.4337 images/s\n",
      "[08/13 14:53:57] ppdet.engine INFO: Epoch: [59] [ 0/36] learning_rate: 0.001768 loss_xy: 1.032661 loss_wh: 1.572400 loss_iou: 4.329158 loss_iou_aware: 0.894647 loss_obj: 7.197979 loss_cls: 0.320682 loss: 15.553589 eta: 1:24:15 batch_cost: 0.8159 data_cost: 0.0003 ips: 14.7084 images/s\n",
      "[08/13 14:54:13] ppdet.engine INFO: Epoch: [59] [20/36] learning_rate: 0.001785 loss_xy: 1.032371 loss_wh: 1.672798 loss_iou: 4.116243 loss_iou_aware: 0.883796 loss_obj: 6.788297 loss_cls: 0.322600 loss: 15.288121 eta: 1:24:02 batch_cost: 0.6673 data_cost: 0.0003 ips: 17.9839 images/s\n",
      "[08/13 14:54:28] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:54:28] ppdet.engine INFO: Epoch: [60] [ 0/36] learning_rate: 0.001798 loss_xy: 0.963430 loss_wh: 1.671443 loss_iou: 4.001928 loss_iou_aware: 0.777581 loss_obj: 6.550289 loss_cls: 0.288470 loss: 15.025537 eta: 1:23:51 batch_cost: 0.6386 data_cost: 0.0002 ips: 18.7907 images/s\n",
      "[08/13 14:54:44] ppdet.engine INFO: Epoch: [60] [20/36] learning_rate: 0.001815 loss_xy: 1.046823 loss_wh: 1.686683 loss_iou: 4.360872 loss_iou_aware: 0.874722 loss_obj: 7.073783 loss_cls: 0.319297 loss: 15.208004 eta: 1:23:38 batch_cost: 0.6673 data_cost: 0.0003 ips: 17.9829 images/s\n",
      "[08/13 14:54:56] ppdet.engine INFO: Epoch: [61] [ 0/36] learning_rate: 0.001828 loss_xy: 0.946211 loss_wh: 1.839603 loss_iou: 4.243107 loss_iou_aware: 0.849188 loss_obj: 6.604669 loss_cls: 0.311511 loss: 15.090496 eta: 1:23:22 batch_cost: 0.5593 data_cost: 0.0324 ips: 21.4560 images/s\n",
      "[08/13 14:55:13] ppdet.engine INFO: Epoch: [61] [20/36] learning_rate: 0.001845 loss_xy: 1.089298 loss_wh: 1.904898 loss_iou: 4.649271 loss_iou_aware: 0.919384 loss_obj: 7.238819 loss_cls: 0.413876 loss: 16.084232 eta: 1:23:11 batch_cost: 0.6992 data_cost: 0.0002 ips: 17.1625 images/s\n",
      "[08/13 14:55:27] ppdet.engine INFO: Epoch: [62] [ 0/36] learning_rate: 0.001858 loss_xy: 1.102990 loss_wh: 1.864620 loss_iou: 4.518041 loss_iou_aware: 0.877884 loss_obj: 7.508260 loss_cls: 0.370901 loss: 16.523624 eta: 1:23:02 batch_cost: 0.7136 data_cost: 0.0074 ips: 16.8165 images/s\n",
      "[08/13 14:55:41] ppdet.engine INFO: Epoch: [62] [20/36] learning_rate: 0.001875 loss_xy: 0.916792 loss_wh: 1.759032 loss_iou: 4.129128 loss_iou_aware: 0.838779 loss_obj: 6.676438 loss_cls: 0.324788 loss: 14.900843 eta: 1:22:42 batch_cost: 0.5731 data_cost: 0.0003 ips: 20.9380 images/s\n",
      "[08/13 14:55:56] ppdet.engine INFO: Epoch: [63] [ 0/36] learning_rate: 0.001888 loss_xy: 1.008857 loss_wh: 1.875718 loss_iou: 4.278060 loss_iou_aware: 0.857825 loss_obj: 7.014091 loss_cls: 0.383372 loss: 15.318889 eta: 1:22:36 batch_cost: 0.7289 data_cost: 0.0393 ips: 16.4626 images/s\n",
      "[08/13 14:56:11] ppdet.engine INFO: Epoch: [63] [20/36] learning_rate: 0.001905 loss_xy: 0.975935 loss_wh: 1.656085 loss_iou: 4.210303 loss_iou_aware: 0.854043 loss_obj: 6.999205 loss_cls: 0.331429 loss: 14.742905 eta: 1:22:21 batch_cost: 0.6377 data_cost: 0.0003 ips: 18.8177 images/s\n",
      "[08/13 14:56:26] ppdet.engine INFO: Epoch: [64] [ 0/36] learning_rate: 0.001918 loss_xy: 1.008613 loss_wh: 1.595134 loss_iou: 4.157089 loss_iou_aware: 0.893445 loss_obj: 6.940197 loss_cls: 0.298488 loss: 14.833912 eta: 1:22:14 batch_cost: 0.6869 data_cost: 0.0560 ips: 17.4687 images/s\n",
      "[08/13 14:56:40] ppdet.engine INFO: Epoch: [64] [20/36] learning_rate: 0.001935 loss_xy: 0.996403 loss_wh: 1.664276 loss_iou: 4.179464 loss_iou_aware: 0.860237 loss_obj: 7.360822 loss_cls: 0.317455 loss: 15.340319 eta: 1:21:57 batch_cost: 0.6153 data_cost: 0.0003 ips: 19.5030 images/s\n",
      "[08/13 14:56:55] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:56:55] ppdet.engine INFO: Epoch: [65] [ 0/36] learning_rate: 0.001948 loss_xy: 0.993259 loss_wh: 1.771541 loss_iou: 4.225079 loss_iou_aware: 0.845162 loss_obj: 6.856285 loss_cls: 0.308856 loss: 15.437443 eta: 1:21:46 batch_cost: 0.6369 data_cost: 0.0002 ips: 18.8424 images/s\n",
      "[08/13 14:57:10] ppdet.engine INFO: Epoch: [65] [20/36] learning_rate: 0.001965 loss_xy: 0.900496 loss_wh: 1.911742 loss_iou: 4.125261 loss_iou_aware: 0.826222 loss_obj: 6.916585 loss_cls: 0.380590 loss: 14.766766 eta: 1:21:29 batch_cost: 0.6089 data_cost: 0.0003 ips: 19.7090 images/s\n",
      "[08/13 14:57:25] ppdet.engine INFO: Epoch: [66] [ 0/36] learning_rate: 0.001978 loss_xy: 0.914329 loss_wh: 2.034573 loss_iou: 4.474587 loss_iou_aware: 0.902083 loss_obj: 6.803921 loss_cls: 0.378619 loss: 15.775607 eta: 1:21:21 batch_cost: 0.7037 data_cost: 0.0157 ips: 17.0527 images/s\n",
      "[08/13 14:57:41] ppdet.engine INFO: Epoch: [66] [20/36] learning_rate: 0.001995 loss_xy: 0.958183 loss_wh: 1.776821 loss_iou: 4.418083 loss_iou_aware: 0.865663 loss_obj: 7.263093 loss_cls: 0.378783 loss: 15.966496 eta: 1:21:10 batch_cost: 0.7017 data_cost: 0.0003 ips: 17.1006 images/s\n",
      "[08/13 14:57:54] ppdet.engine INFO: Epoch: [67] [ 0/36] learning_rate: 0.002008 loss_xy: 1.080581 loss_wh: 1.901096 loss_iou: 4.330621 loss_iou_aware: 0.901847 loss_obj: 7.088720 loss_cls: 0.364975 loss: 15.495137 eta: 1:20:58 batch_cost: 0.6757 data_cost: 0.0079 ips: 17.7584 images/s\n",
      "[08/13 14:58:07] ppdet.engine INFO: Epoch: [67] [20/36] learning_rate: 0.002025 loss_xy: 0.979407 loss_wh: 1.690690 loss_iou: 4.365212 loss_iou_aware: 0.929579 loss_obj: 7.011022 loss_cls: 0.357563 loss: 15.388976 eta: 1:20:38 batch_cost: 0.5572 data_cost: 0.0002 ips: 21.5363 images/s\n",
      "[08/13 14:58:20] ppdet.engine INFO: Epoch: [68] [ 0/36] learning_rate: 0.002038 loss_xy: 0.939208 loss_wh: 1.588795 loss_iou: 4.245921 loss_iou_aware: 0.881248 loss_obj: 6.866578 loss_cls: 0.334242 loss: 14.878460 eta: 1:20:27 batch_cost: 0.6412 data_cost: 0.0170 ips: 18.7141 images/s\n",
      "[08/13 14:58:36] ppdet.engine INFO: Epoch: [68] [20/36] learning_rate: 0.002055 loss_xy: 0.971038 loss_wh: 1.509760 loss_iou: 4.141066 loss_iou_aware: 0.875516 loss_obj: 6.666022 loss_cls: 0.277062 loss: 15.135690 eta: 1:20:12 batch_cost: 0.6380 data_cost: 0.0003 ips: 18.8098 images/s\n",
      "[08/13 14:58:50] ppdet.engine INFO: Epoch: [69] [ 0/36] learning_rate: 0.002068 loss_xy: 1.029287 loss_wh: 1.573153 loss_iou: 4.417250 loss_iou_aware: 0.937834 loss_obj: 6.810515 loss_cls: 0.312345 loss: 15.515119 eta: 1:20:02 batch_cost: 0.6581 data_cost: 0.0359 ips: 18.2346 images/s\n",
      "[08/13 14:59:03] ppdet.engine INFO: Epoch: [69] [20/36] learning_rate: 0.002085 loss_xy: 0.888609 loss_wh: 1.419853 loss_iou: 3.796977 loss_iou_aware: 0.805279 loss_obj: 6.154957 loss_cls: 0.315251 loss: 13.294201 eta: 1:19:40 batch_cost: 0.5034 data_cost: 0.0003 ips: 23.8361 images/s\n",
      "[08/13 14:59:18] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 14:59:19] ppdet.engine INFO: Epoch: [70] [ 0/36] learning_rate: 0.002098 loss_xy: 1.012764 loss_wh: 1.491588 loss_iou: 4.095243 loss_iou_aware: 0.882728 loss_obj: 6.427607 loss_cls: 0.283475 loss: 14.494007 eta: 1:19:28 batch_cost: 0.6047 data_cost: 0.0003 ips: 19.8449 images/s\n",
      "[08/13 14:59:35] ppdet.engine INFO: Epoch: [70] [20/36] learning_rate: 0.002115 loss_xy: 0.918850 loss_wh: 1.967467 loss_iou: 4.429192 loss_iou_aware: 0.884027 loss_obj: 6.704744 loss_cls: 0.368700 loss: 15.127439 eta: 1:19:15 batch_cost: 0.6603 data_cost: 0.0003 ips: 18.1744 images/s\n",
      "[08/13 14:59:50] ppdet.engine INFO: Epoch: [71] [ 0/36] learning_rate: 0.002128 loss_xy: 0.970102 loss_wh: 1.910376 loss_iou: 4.383956 loss_iou_aware: 0.853166 loss_obj: 7.069882 loss_cls: 0.411123 loss: 15.333442 eta: 1:19:09 batch_cost: 0.6887 data_cost: 0.0431 ips: 17.4247 images/s\n",
      "[08/13 15:00:06] ppdet.engine INFO: Epoch: [71] [20/36] learning_rate: 0.002145 loss_xy: 1.087977 loss_wh: 1.644218 loss_iou: 4.300749 loss_iou_aware: 0.929057 loss_obj: 7.189273 loss_cls: 0.376421 loss: 15.111219 eta: 1:18:58 batch_cost: 0.6988 data_cost: 0.0003 ips: 17.1719 images/s\n",
      "[08/13 15:00:20] ppdet.engine INFO: Epoch: [72] [ 0/36] learning_rate: 0.002158 loss_xy: 0.961483 loss_wh: 1.429842 loss_iou: 4.001436 loss_iou_aware: 0.874871 loss_obj: 6.642490 loss_cls: 0.271292 loss: 14.332305 eta: 1:18:47 batch_cost: 0.6950 data_cost: 0.0003 ips: 17.2657 images/s\n",
      "[08/13 15:00:34] ppdet.engine INFO: Epoch: [72] [20/36] learning_rate: 0.002174 loss_xy: 0.946017 loss_wh: 1.348316 loss_iou: 3.897730 loss_iou_aware: 0.834786 loss_obj: 6.301119 loss_cls: 0.275330 loss: 13.810150 eta: 1:18:29 batch_cost: 0.5788 data_cost: 0.0003 ips: 20.7308 images/s\n",
      "[08/13 15:00:46] ppdet.engine INFO: Epoch: [73] [ 0/36] learning_rate: 0.002188 loss_xy: 0.935657 loss_wh: 1.456957 loss_iou: 3.980219 loss_iou_aware: 0.848973 loss_obj: 6.318236 loss_cls: 0.264473 loss: 14.419184 eta: 1:18:15 batch_cost: 0.5814 data_cost: 0.0177 ips: 20.6395 images/s\n",
      "[08/13 15:01:02] ppdet.engine INFO: Epoch: [73] [20/36] learning_rate: 0.002204 loss_xy: 0.890107 loss_wh: 1.629808 loss_iou: 3.942149 loss_iou_aware: 0.841489 loss_obj: 6.625342 loss_cls: 0.397585 loss: 14.498610 eta: 1:18:02 batch_cost: 0.6622 data_cost: 0.0003 ips: 18.1217 images/s\n",
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      "[08/13 15:01:32] ppdet.engine INFO: Epoch: [74] [20/36] learning_rate: 0.002234 loss_xy: 0.943645 loss_wh: 1.670093 loss_iou: 4.208158 loss_iou_aware: 0.862802 loss_obj: 6.394731 loss_cls: 0.307904 loss: 14.767666 eta: 1:17:43 batch_cost: 0.6893 data_cost: 0.0003 ips: 17.4088 images/s\n",
      "[08/13 15:01:47] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:01:48] ppdet.engine INFO: Epoch: [75] [ 0/36] learning_rate: 0.002248 loss_xy: 0.925999 loss_wh: 1.503915 loss_iou: 4.150879 loss_iou_aware: 0.892529 loss_obj: 6.740108 loss_cls: 0.313286 loss: 14.709372 eta: 1:17:33 batch_cost: 0.6778 data_cost: 0.0002 ips: 17.7030 images/s\n",
      "[08/13 15:02:03] ppdet.engine INFO: Epoch: [75] [20/36] learning_rate: 0.002264 loss_xy: 0.991236 loss_wh: 1.586816 loss_iou: 4.170774 loss_iou_aware: 0.878119 loss_obj: 6.328089 loss_cls: 0.330546 loss: 14.126533 eta: 1:17:19 batch_cost: 0.6363 data_cost: 0.0003 ips: 18.8580 images/s\n",
      "[08/13 15:02:17] ppdet.engine INFO: Epoch: [76] [ 0/36] learning_rate: 0.002278 loss_xy: 0.955470 loss_wh: 1.612744 loss_iou: 4.068995 loss_iou_aware: 0.832362 loss_obj: 6.346498 loss_cls: 0.322707 loss: 13.995672 eta: 1:17:09 batch_cost: 0.6575 data_cost: 0.0563 ips: 18.2513 images/s\n",
      "[08/13 15:02:33] ppdet.engine INFO: Epoch: [76] [20/36] learning_rate: 0.002294 loss_xy: 0.986145 loss_wh: 1.509390 loss_iou: 3.814891 loss_iou_aware: 0.848358 loss_obj: 6.939565 loss_cls: 0.296293 loss: 14.728287 eta: 1:16:55 batch_cost: 0.6537 data_cost: 0.0003 ips: 18.3566 images/s\n",
      "[08/13 15:02:47] ppdet.engine INFO: Epoch: [77] [ 0/36] learning_rate: 0.002308 loss_xy: 1.008264 loss_wh: 1.397557 loss_iou: 3.771314 loss_iou_aware: 0.854442 loss_obj: 6.545609 loss_cls: 0.295087 loss: 14.027977 eta: 1:16:47 batch_cost: 0.7247 data_cost: 0.0588 ips: 16.5580 images/s\n",
      "[08/13 15:03:03] ppdet.engine INFO: Epoch: [77] [20/36] learning_rate: 0.002324 loss_xy: 1.013276 loss_wh: 1.509843 loss_iou: 4.174868 loss_iou_aware: 0.910905 loss_obj: 6.605526 loss_cls: 0.306196 loss: 14.708575 eta: 1:16:36 batch_cost: 0.6947 data_cost: 0.0003 ips: 17.2733 images/s\n",
      "[08/13 15:03:17] ppdet.engine INFO: Epoch: [78] [ 0/36] learning_rate: 0.002338 loss_xy: 0.994526 loss_wh: 1.816404 loss_iou: 4.235270 loss_iou_aware: 0.872686 loss_obj: 6.549480 loss_cls: 0.327416 loss: 15.105812 eta: 1:16:26 batch_cost: 0.6819 data_cost: 0.0609 ips: 17.5991 images/s\n",
      "[08/13 15:03:32] ppdet.engine INFO: Epoch: [78] [20/36] learning_rate: 0.002354 loss_xy: 0.960098 loss_wh: 1.664531 loss_iou: 4.344786 loss_iou_aware: 0.898003 loss_obj: 6.488738 loss_cls: 0.366635 loss: 14.771286 eta: 1:16:10 batch_cost: 0.6170 data_cost: 0.0003 ips: 19.4496 images/s\n",
      "[08/13 15:03:46] ppdet.engine INFO: Epoch: [79] [ 0/36] learning_rate: 0.002368 loss_xy: 0.955536 loss_wh: 1.609851 loss_iou: 4.601527 loss_iou_aware: 0.988573 loss_obj: 7.500738 loss_cls: 0.372661 loss: 15.665316 eta: 1:16:00 batch_cost: 0.6779 data_cost: 0.0164 ips: 17.7029 images/s\n",
      "[08/13 15:04:01] ppdet.engine INFO: Epoch: [79] [20/36] learning_rate: 0.002384 loss_xy: 0.961834 loss_wh: 1.671539 loss_iou: 4.133878 loss_iou_aware: 0.855398 loss_obj: 6.259498 loss_cls: 0.315165 loss: 14.049446 eta: 1:15:46 batch_cost: 0.6487 data_cost: 0.0002 ips: 18.4980 images/s\n",
      "[08/13 15:04:14] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:04:15] ppdet.engine INFO: Epoch: [80] [ 0/36] learning_rate: 0.002398 loss_xy: 0.964293 loss_wh: 1.428663 loss_iou: 3.891355 loss_iou_aware: 0.828756 loss_obj: 6.525945 loss_cls: 0.333417 loss: 13.938881 eta: 1:15:35 batch_cost: 0.6271 data_cost: 0.0219 ips: 19.1356 images/s\n",
      "[08/13 15:04:28] ppdet.engine INFO: Epoch: [80] [20/36] learning_rate: 0.002414 loss_xy: 0.946075 loss_wh: 1.556495 loss_iou: 4.059773 loss_iou_aware: 0.878779 loss_obj: 6.589484 loss_cls: 0.288383 loss: 14.442213 eta: 1:15:17 batch_cost: 0.5596 data_cost: 0.0003 ips: 21.4450 images/s\n",
      "[08/13 15:04:42] ppdet.engine INFO: Epoch: [81] [ 0/36] learning_rate: 0.002428 loss_xy: 0.971032 loss_wh: 1.263255 loss_iou: 3.755372 loss_iou_aware: 0.818417 loss_obj: 6.375394 loss_cls: 0.283260 loss: 13.519687 eta: 1:15:06 batch_cost: 0.6307 data_cost: 0.0132 ips: 19.0274 images/s\n",
      "[08/13 15:04:58] ppdet.engine INFO: Epoch: [81] [20/36] learning_rate: 0.002444 loss_xy: 0.957365 loss_wh: 1.521033 loss_iou: 3.807114 loss_iou_aware: 0.823189 loss_obj: 6.070780 loss_cls: 0.297954 loss: 13.298103 eta: 1:14:52 batch_cost: 0.6469 data_cost: 0.0003 ips: 18.5501 images/s\n",
      "[08/13 15:05:11] ppdet.engine INFO: Epoch: [82] [ 0/36] learning_rate: 0.002458 loss_xy: 0.989098 loss_wh: 1.467620 loss_iou: 4.179914 loss_iou_aware: 0.867051 loss_obj: 6.332973 loss_cls: 0.278171 loss: 13.881756 eta: 1:14:42 batch_cost: 0.6633 data_cost: 0.0240 ips: 18.0913 images/s\n",
      "[08/13 15:05:28] ppdet.engine INFO: Epoch: [82] [20/36] learning_rate: 0.002474 loss_xy: 0.957340 loss_wh: 1.294616 loss_iou: 3.820998 loss_iou_aware: 0.855756 loss_obj: 7.063852 loss_cls: 0.359635 loss: 14.869184 eta: 1:14:30 batch_cost: 0.6965 data_cost: 0.0003 ips: 17.2302 images/s\n",
      "[08/13 15:05:42] ppdet.engine INFO: Epoch: [83] [ 0/36] learning_rate: 0.002488 loss_xy: 0.945759 loss_wh: 1.457622 loss_iou: 3.982054 loss_iou_aware: 0.815594 loss_obj: 6.721230 loss_cls: 0.265074 loss: 14.435896 eta: 1:14:22 batch_cost: 0.7389 data_cost: 0.0673 ips: 16.2412 images/s\n",
      "[08/13 15:05:57] ppdet.engine INFO: Epoch: [83] [20/36] learning_rate: 0.002504 loss_xy: 0.952055 loss_wh: 1.442970 loss_iou: 3.990654 loss_iou_aware: 0.850150 loss_obj: 6.658517 loss_cls: 0.314471 loss: 14.269992 eta: 1:14:05 batch_cost: 0.5810 data_cost: 0.0003 ips: 20.6552 images/s\n",
      "[08/13 15:06:11] ppdet.engine INFO: Epoch: [84] [ 0/36] learning_rate: 0.002517 loss_xy: 0.935271 loss_wh: 1.431911 loss_iou: 3.871581 loss_iou_aware: 0.856332 loss_obj: 6.476898 loss_cls: 0.295669 loss: 14.107292 eta: 1:13:55 batch_cost: 0.6719 data_cost: 0.0235 ips: 17.8597 images/s\n",
      "[08/13 15:06:22] ppdet.engine INFO: Epoch: [84] [20/36] learning_rate: 0.002534 loss_xy: 0.903883 loss_wh: 1.371275 loss_iou: 3.975539 loss_iou_aware: 0.848173 loss_obj: 5.977967 loss_cls: 0.303745 loss: 13.674026 eta: 1:13:33 batch_cost: 0.4621 data_cost: 0.0022 ips: 25.9688 images/s\n",
      "[08/13 15:06:36] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:06:37] ppdet.engine INFO: Epoch: [85] [ 0/36] learning_rate: 0.002547 loss_xy: 0.949478 loss_wh: 1.234232 loss_iou: 3.580291 loss_iou_aware: 0.827148 loss_obj: 5.977967 loss_cls: 0.258327 loss: 12.449327 eta: 1:13:22 batch_cost: 0.6293 data_cost: 0.0003 ips: 19.0686 images/s\n",
      "[08/13 15:06:55] ppdet.engine INFO: Epoch: [85] [20/36] learning_rate: 0.002564 loss_xy: 0.976580 loss_wh: 1.614442 loss_iou: 4.178208 loss_iou_aware: 0.896901 loss_obj: 6.545904 loss_cls: 0.282659 loss: 14.749733 eta: 1:13:12 batch_cost: 0.7436 data_cost: 0.0138 ips: 16.1366 images/s\n",
      "[08/13 15:07:07] ppdet.engine INFO: Epoch: [86] [ 0/36] learning_rate: 0.002577 loss_xy: 0.946587 loss_wh: 1.688936 loss_iou: 4.307932 loss_iou_aware: 0.883024 loss_obj: 6.248053 loss_cls: 0.302578 loss: 14.380024 eta: 1:12:59 batch_cost: 0.6193 data_cost: 0.0224 ips: 19.3758 images/s\n",
      "[08/13 15:07:25] ppdet.engine INFO: Epoch: [86] [20/36] learning_rate: 0.002594 loss_xy: 0.934563 loss_wh: 1.358968 loss_iou: 3.811146 loss_iou_aware: 0.839217 loss_obj: 5.886436 loss_cls: 0.274344 loss: 12.903488 eta: 1:12:51 batch_cost: 0.7768 data_cost: 0.1209 ips: 15.4476 images/s\n",
      "[08/13 15:07:41] ppdet.engine INFO: Epoch: [87] [ 0/36] learning_rate: 0.002607 loss_xy: 0.908499 loss_wh: 1.378941 loss_iou: 4.015529 loss_iou_aware: 0.826030 loss_obj: 5.774444 loss_cls: 0.248024 loss: 12.979122 eta: 1:12:45 batch_cost: 0.8012 data_cost: 0.0636 ips: 14.9783 images/s\n",
      "[08/13 15:07:56] ppdet.engine INFO: Epoch: [87] [20/36] learning_rate: 0.002624 loss_xy: 0.858990 loss_wh: 1.463355 loss_iou: 3.881901 loss_iou_aware: 0.814099 loss_obj: 6.176089 loss_cls: 0.288939 loss: 13.356733 eta: 1:12:31 batch_cost: 0.6482 data_cost: 0.0587 ips: 18.5117 images/s\n",
      "[08/13 15:08:12] ppdet.engine INFO: Epoch: [88] [ 0/36] learning_rate: 0.002637 loss_xy: 0.896368 loss_wh: 1.100187 loss_iou: 3.684971 loss_iou_aware: 0.828437 loss_obj: 6.324980 loss_cls: 0.274210 loss: 13.457153 eta: 1:12:22 batch_cost: 0.7099 data_cost: 0.0500 ips: 16.9040 images/s\n",
      "[08/13 15:08:30] ppdet.engine INFO: Epoch: [88] [20/36] learning_rate: 0.002654 loss_xy: 0.894975 loss_wh: 1.257628 loss_iou: 3.759291 loss_iou_aware: 0.868470 loss_obj: 6.442342 loss_cls: 0.207311 loss: 13.527357 eta: 1:12:15 batch_cost: 0.8074 data_cost: 0.1378 ips: 14.8629 images/s\n",
      "[08/13 15:08:45] ppdet.engine INFO: Epoch: [89] [ 0/36] learning_rate: 0.002667 loss_xy: 0.909592 loss_wh: 1.437617 loss_iou: 3.877909 loss_iou_aware: 0.837233 loss_obj: 6.292306 loss_cls: 0.244266 loss: 13.689161 eta: 1:12:07 batch_cost: 0.7574 data_cost: 0.0369 ips: 15.8446 images/s\n",
      "[08/13 15:09:04] ppdet.engine INFO: Epoch: [89] [20/36] learning_rate: 0.002684 loss_xy: 0.992905 loss_wh: 1.358173 loss_iou: 3.778698 loss_iou_aware: 0.838099 loss_obj: 5.844296 loss_cls: 0.293072 loss: 13.567432 eta: 1:11:59 batch_cost: 0.8073 data_cost: 0.0606 ips: 14.8635 images/s\n",
      "[08/13 15:09:20] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:09:21] ppdet.engine INFO: Epoch: [90] [ 0/36] learning_rate: 0.002697 loss_xy: 0.967335 loss_wh: 1.259587 loss_iou: 3.629364 loss_iou_aware: 0.839446 loss_obj: 6.266579 loss_cls: 0.301114 loss: 13.505795 eta: 1:11:50 batch_cost: 0.7245 data_cost: 0.0002 ips: 16.5621 images/s\n",
      "[08/13 15:09:38] ppdet.engine INFO: Epoch: [90] [20/36] learning_rate: 0.002714 loss_xy: 0.894934 loss_wh: 1.388761 loss_iou: 3.670826 loss_iou_aware: 0.784865 loss_obj: 5.899787 loss_cls: 0.270344 loss: 13.234329 eta: 1:11:39 batch_cost: 0.7099 data_cost: 0.0003 ips: 16.9032 images/s\n",
      "[08/13 15:09:52] ppdet.engine INFO: Epoch: [91] [ 0/36] learning_rate: 0.002727 loss_xy: 0.949593 loss_wh: 1.528043 loss_iou: 3.976437 loss_iou_aware: 0.816652 loss_obj: 6.521502 loss_cls: 0.291702 loss: 14.176743 eta: 1:11:30 batch_cost: 0.7021 data_cost: 0.0350 ips: 17.0915 images/s\n",
      "[08/13 15:10:08] ppdet.engine INFO: Epoch: [91] [20/36] learning_rate: 0.002744 loss_xy: 0.906621 loss_wh: 1.395394 loss_iou: 3.714610 loss_iou_aware: 0.806955 loss_obj: 6.087031 loss_cls: 0.234996 loss: 13.361596 eta: 1:11:17 batch_cost: 0.6902 data_cost: 0.0003 ips: 17.3866 images/s\n",
      "[08/13 15:10:23] ppdet.engine INFO: Epoch: [92] [ 0/36] learning_rate: 0.002757 loss_xy: 0.904410 loss_wh: 1.350659 loss_iou: 3.658574 loss_iou_aware: 0.823666 loss_obj: 6.200007 loss_cls: 0.337129 loss: 13.183982 eta: 1:11:08 batch_cost: 0.6672 data_cost: 0.0003 ips: 17.9860 images/s\n",
      "[08/13 15:10:40] ppdet.engine INFO: Epoch: [92] [20/36] learning_rate: 0.002774 loss_xy: 0.934369 loss_wh: 1.425800 loss_iou: 3.861016 loss_iou_aware: 0.869707 loss_obj: 6.298079 loss_cls: 0.319914 loss: 13.847738 eta: 1:10:57 batch_cost: 0.7192 data_cost: 0.0010 ips: 16.6841 images/s\n",
      "[08/13 15:10:55] ppdet.engine INFO: Epoch: [93] [ 0/36] learning_rate: 0.002787 loss_xy: 0.948532 loss_wh: 1.424963 loss_iou: 3.759559 loss_iou_aware: 0.844325 loss_obj: 6.522676 loss_cls: 0.321545 loss: 14.267714 eta: 1:10:48 batch_cost: 0.7304 data_cost: 0.0439 ips: 16.4298 images/s\n",
      "[08/13 15:11:11] ppdet.engine INFO: Epoch: [93] [20/36] learning_rate: 0.002804 loss_xy: 0.866941 loss_wh: 1.293487 loss_iou: 3.782948 loss_iou_aware: 0.818675 loss_obj: 5.758561 loss_cls: 0.257438 loss: 12.967695 eta: 1:10:34 batch_cost: 0.6633 data_cost: 0.0003 ips: 18.0903 images/s\n",
      "[08/13 15:11:27] ppdet.engine INFO: Epoch: [94] [ 0/36] learning_rate: 0.002817 loss_xy: 0.882292 loss_wh: 1.299943 loss_iou: 3.738805 loss_iou_aware: 0.790188 loss_obj: 6.125551 loss_cls: 0.251846 loss: 12.754845 eta: 1:10:27 batch_cost: 0.7738 data_cost: 0.0004 ips: 15.5077 images/s\n",
      "[08/13 15:11:44] ppdet.engine INFO: Epoch: [94] [20/36] learning_rate: 0.002834 loss_xy: 0.870294 loss_wh: 1.445173 loss_iou: 3.899731 loss_iou_aware: 0.817926 loss_obj: 5.914432 loss_cls: 0.255491 loss: 13.748857 eta: 1:10:15 batch_cost: 0.6980 data_cost: 0.0003 ips: 17.1920 images/s\n",
      "[08/13 15:11:59] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:12:00] ppdet.engine INFO: Epoch: [95] [ 0/36] learning_rate: 0.002847 loss_xy: 0.835249 loss_wh: 1.440308 loss_iou: 4.025025 loss_iou_aware: 0.891706 loss_obj: 5.899284 loss_cls: 0.261787 loss: 13.869896 eta: 1:10:03 batch_cost: 0.6926 data_cost: 0.0002 ips: 17.3251 images/s\n",
      "[08/13 15:12:17] ppdet.engine INFO: Epoch: [95] [20/36] learning_rate: 0.002864 loss_xy: 0.875226 loss_wh: 1.460087 loss_iou: 3.841931 loss_iou_aware: 0.813301 loss_obj: 5.965423 loss_cls: 0.263941 loss: 13.028507 eta: 1:09:52 batch_cost: 0.7323 data_cost: 0.0002 ips: 16.3878 images/s\n",
      "[08/13 15:12:30] ppdet.engine INFO: Epoch: [96] [ 0/36] learning_rate: 0.002877 loss_xy: 0.926584 loss_wh: 1.620321 loss_iou: 4.215391 loss_iou_aware: 0.869027 loss_obj: 6.072198 loss_cls: 0.372417 loss: 14.104399 eta: 1:09:42 batch_cost: 0.6899 data_cost: 0.0670 ips: 17.3927 images/s\n",
      "[08/13 15:12:46] ppdet.engine INFO: Epoch: [96] [20/36] learning_rate: 0.002894 loss_xy: 0.869282 loss_wh: 1.373939 loss_iou: 3.727672 loss_iou_aware: 0.817224 loss_obj: 5.791627 loss_cls: 0.277075 loss: 12.813519 eta: 1:09:28 batch_cost: 0.6564 data_cost: 0.0003 ips: 18.2810 images/s\n",
      "[08/13 15:12:59] ppdet.engine INFO: Epoch: [97] [ 0/36] learning_rate: 0.002907 loss_xy: 0.898534 loss_wh: 1.494517 loss_iou: 3.703823 loss_iou_aware: 0.856080 loss_obj: 5.961741 loss_cls: 0.277577 loss: 13.266338 eta: 1:09:17 batch_cost: 0.6505 data_cost: 0.0158 ips: 18.4471 images/s\n",
      "[08/13 15:13:16] ppdet.engine INFO: Epoch: [97] [20/36] learning_rate: 0.002924 loss_xy: 0.956180 loss_wh: 1.518785 loss_iou: 4.183344 loss_iou_aware: 0.901041 loss_obj: 6.215920 loss_cls: 0.370095 loss: 14.305859 eta: 1:09:04 batch_cost: 0.6853 data_cost: 0.0003 ips: 17.5103 images/s\n",
      "[08/13 15:13:29] ppdet.engine INFO: Epoch: [98] [ 0/36] learning_rate: 0.002937 loss_xy: 0.902826 loss_wh: 1.240296 loss_iou: 3.661254 loss_iou_aware: 0.832778 loss_obj: 5.982815 loss_cls: 0.253921 loss: 13.078569 eta: 1:08:51 batch_cost: 0.6017 data_cost: 0.0506 ips: 19.9430 images/s\n",
      "[08/13 15:13:45] ppdet.engine INFO: Epoch: [98] [20/36] learning_rate: 0.002954 loss_xy: 0.895836 loss_wh: 1.577824 loss_iou: 3.976944 loss_iou_aware: 0.849531 loss_obj: 6.304552 loss_cls: 0.245266 loss: 14.021867 eta: 1:08:39 batch_cost: 0.6838 data_cost: 0.0003 ips: 17.5478 images/s\n",
      "[08/13 15:13:58] ppdet.engine INFO: Epoch: [99] [ 0/36] learning_rate: 0.002967 loss_xy: 0.921165 loss_wh: 1.856521 loss_iou: 4.507516 loss_iou_aware: 0.907316 loss_obj: 6.981011 loss_cls: 0.360769 loss: 15.037658 eta: 1:08:27 batch_cost: 0.6699 data_cost: 0.0139 ips: 17.9127 images/s\n",
      "[08/13 15:14:14] ppdet.engine INFO: Epoch: [99] [20/36] learning_rate: 0.002984 loss_xy: 0.862337 loss_wh: 1.577449 loss_iou: 3.992031 loss_iou_aware: 0.857558 loss_obj: 6.145736 loss_cls: 0.331494 loss: 13.718382 eta: 1:08:14 batch_cost: 0.6578 data_cost: 0.0003 ips: 18.2438 images/s\n",
      "[08/13 15:14:27] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:14:28] ppdet.engine INFO: Epoch: [100] [ 0/36] learning_rate: 0.002997 loss_xy: 0.891758 loss_wh: 1.453731 loss_iou: 4.051089 loss_iou_aware: 0.894367 loss_obj: 5.773630 loss_cls: 0.289888 loss: 13.711958 eta: 1:08:01 batch_cost: 0.5817 data_cost: 0.0003 ips: 20.6294 images/s\n",
      "[08/13 15:14:44] ppdet.engine INFO: Epoch: [100] [20/36] learning_rate: 0.003014 loss_xy: 0.779984 loss_wh: 1.227251 loss_iou: 3.417966 loss_iou_aware: 0.787250 loss_obj: 5.643986 loss_cls: 0.287024 loss: 12.119435 eta: 1:07:49 batch_cost: 0.7332 data_cost: 0.0003 ips: 16.3660 images/s\n",
      "[08/13 15:14:58] ppdet.engine INFO: Epoch: [101] [ 0/36] learning_rate: 0.003027 loss_xy: 0.850068 loss_wh: 1.274820 loss_iou: 3.538495 loss_iou_aware: 0.803091 loss_obj: 6.389921 loss_cls: 0.266824 loss: 12.783213 eta: 1:07:38 batch_cost: 0.7127 data_cost: 0.0126 ips: 16.8385 images/s\n",
      "[08/13 15:15:15] ppdet.engine INFO: Epoch: [101] [20/36] learning_rate: 0.003044 loss_xy: 0.923132 loss_wh: 1.142073 loss_iou: 3.628231 loss_iou_aware: 0.811769 loss_obj: 6.046251 loss_cls: 0.224593 loss: 12.955593 eta: 1:07:27 batch_cost: 0.7284 data_cost: 0.0168 ips: 16.4748 images/s\n",
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      "[08/13 15:16:03] ppdet.engine INFO: Epoch: [103] [ 0/36] learning_rate: 0.003087 loss_xy: 0.932322 loss_wh: 1.381611 loss_iou: 3.981800 loss_iou_aware: 0.873408 loss_obj: 6.557485 loss_cls: 0.270711 loss: 14.115293 eta: 1:06:58 batch_cost: 0.8443 data_cost: 0.1504 ips: 14.2133 images/s\n",
      "[08/13 15:16:21] ppdet.engine INFO: Epoch: [103] [20/36] learning_rate: 0.003104 loss_xy: 0.828260 loss_wh: 1.249350 loss_iou: 3.649931 loss_iou_aware: 0.816285 loss_obj: 5.858862 loss_cls: 0.223871 loss: 12.392221 eta: 1:06:49 batch_cost: 0.7942 data_cost: 0.1217 ips: 15.1098 images/s\n",
      "[08/13 15:16:35] ppdet.engine INFO: Epoch: [104] [ 0/36] learning_rate: 0.003117 loss_xy: 0.869760 loss_wh: 1.276749 loss_iou: 3.620764 loss_iou_aware: 0.824670 loss_obj: 6.012081 loss_cls: 0.207994 loss: 12.898809 eta: 1:06:39 batch_cost: 0.6907 data_cost: 0.0052 ips: 17.3725 images/s\n",
      "[08/13 15:16:51] ppdet.engine INFO: Epoch: [104] [20/36] learning_rate: 0.003134 loss_xy: 0.888280 loss_wh: 1.624064 loss_iou: 3.829187 loss_iou_aware: 0.822129 loss_obj: 6.332581 loss_cls: 0.315864 loss: 13.965732 eta: 1:06:24 batch_cost: 0.6236 data_cost: 0.0003 ips: 19.2416 images/s\n",
      "[08/13 15:17:05] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:17:05] ppdet.engine INFO: Epoch: [105] [ 0/36] learning_rate: 0.003147 loss_xy: 0.895428 loss_wh: 1.342794 loss_iou: 3.701768 loss_iou_aware: 0.844535 loss_obj: 6.541511 loss_cls: 0.267623 loss: 13.320686 eta: 1:06:11 batch_cost: 0.6129 data_cost: 0.0003 ips: 19.5785 images/s\n",
      "[08/13 15:17:22] ppdet.engine INFO: Epoch: [105] [20/36] learning_rate: 0.003164 loss_xy: 0.948139 loss_wh: 1.294442 loss_iou: 3.687927 loss_iou_aware: 0.831638 loss_obj: 6.337204 loss_cls: 0.267317 loss: 13.863834 eta: 1:06:00 batch_cost: 0.7144 data_cost: 0.0003 ips: 16.7985 images/s\n",
      "[08/13 15:17:35] ppdet.engine INFO: Epoch: [106] [ 0/36] learning_rate: 0.003177 loss_xy: 0.861090 loss_wh: 1.397389 loss_iou: 3.818798 loss_iou_aware: 0.836483 loss_obj: 6.058745 loss_cls: 0.308964 loss: 13.070278 eta: 1:05:49 batch_cost: 0.6751 data_cost: 0.0222 ips: 17.7744 images/s\n",
      "[08/13 15:17:49] ppdet.engine INFO: Epoch: [106] [20/36] learning_rate: 0.003193 loss_xy: 0.938581 loss_wh: 1.326822 loss_iou: 3.753111 loss_iou_aware: 0.848342 loss_obj: 5.833118 loss_cls: 0.261196 loss: 13.369580 eta: 1:05:32 batch_cost: 0.5562 data_cost: 0.0003 ips: 21.5753 images/s\n",
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      "[08/13 15:18:18] ppdet.engine INFO: Epoch: [107] [20/36] learning_rate: 0.003223 loss_xy: 0.856009 loss_wh: 1.310672 loss_iou: 3.744662 loss_iou_aware: 0.843115 loss_obj: 5.915337 loss_cls: 0.297615 loss: 13.123444 eta: 1:05:06 batch_cost: 0.6421 data_cost: 0.0003 ips: 18.6878 images/s\n",
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      "[08/13 15:18:46] ppdet.engine INFO: Epoch: [108] [20/36] learning_rate: 0.003253 loss_xy: 0.979495 loss_wh: 1.805386 loss_iou: 4.378061 loss_iou_aware: 0.944717 loss_obj: 6.598707 loss_cls: 0.360264 loss: 14.776643 eta: 1:04:40 batch_cost: 0.6140 data_cost: 0.0003 ips: 19.5445 images/s\n",
      "[08/13 15:19:00] ppdet.engine INFO: Epoch: [109] [ 0/36] learning_rate: 0.003267 loss_xy: 0.895032 loss_wh: 1.216705 loss_iou: 3.540639 loss_iou_aware: 0.791935 loss_obj: 5.950542 loss_cls: 0.338356 loss: 13.101531 eta: 1:04:29 batch_cost: 0.6325 data_cost: 0.0122 ips: 18.9737 images/s\n",
      "[08/13 15:19:16] ppdet.engine INFO: Epoch: [109] [20/36] learning_rate: 0.003283 loss_xy: 0.905578 loss_wh: 1.307632 loss_iou: 3.809411 loss_iou_aware: 0.871350 loss_obj: 6.322624 loss_cls: 0.233529 loss: 13.276664 eta: 1:04:16 batch_cost: 0.6835 data_cost: 0.0003 ips: 17.5559 images/s\n",
      "[08/13 15:19:30] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:19:31] ppdet.engine INFO: Epoch: [110] [ 0/36] learning_rate: 0.003297 loss_xy: 0.839313 loss_wh: 1.116973 loss_iou: 3.286272 loss_iou_aware: 0.774267 loss_obj: 5.547473 loss_cls: 0.211741 loss: 11.588738 eta: 1:04:05 batch_cost: 0.6827 data_cost: 0.0002 ips: 17.5777 images/s\n",
      "[08/13 15:19:48] ppdet.engine INFO: Epoch: [110] [20/36] learning_rate: 0.003313 loss_xy: 0.823123 loss_wh: 1.208509 loss_iou: 3.536709 loss_iou_aware: 0.782349 loss_obj: 5.788278 loss_cls: 0.237645 loss: 12.301374 eta: 1:03:53 batch_cost: 0.7099 data_cost: 0.0003 ips: 16.9034 images/s\n",
      "[08/13 15:20:02] ppdet.engine INFO: Epoch: [111] [ 0/36] learning_rate: 0.003327 loss_xy: 0.889447 loss_wh: 1.269604 loss_iou: 3.581501 loss_iou_aware: 0.810206 loss_obj: 6.089070 loss_cls: 0.295517 loss: 13.181517 eta: 1:03:42 batch_cost: 0.6644 data_cost: 0.0129 ips: 18.0622 images/s\n",
      "[08/13 15:20:18] ppdet.engine INFO: Epoch: [111] [20/36] learning_rate: 0.003330 loss_xy: 0.912349 loss_wh: 1.318799 loss_iou: 3.811063 loss_iou_aware: 0.857437 loss_obj: 6.640823 loss_cls: 0.243222 loss: 13.655720 eta: 1:03:30 batch_cost: 0.7309 data_cost: 0.0002 ips: 16.4184 images/s\n",
      "[08/13 15:20:32] ppdet.engine INFO: Epoch: [112] [ 0/36] learning_rate: 0.003330 loss_xy: 0.905193 loss_wh: 1.469218 loss_iou: 4.046134 loss_iou_aware: 0.888364 loss_obj: 6.318159 loss_cls: 0.288230 loss: 13.941710 eta: 1:03:20 batch_cost: 0.6971 data_cost: 0.0584 ips: 17.2143 images/s\n",
      "[08/13 15:20:49] ppdet.engine INFO: Epoch: [112] [20/36] learning_rate: 0.003330 loss_xy: 1.023605 loss_wh: 1.560839 loss_iou: 4.053155 loss_iou_aware: 0.956214 loss_obj: 6.743894 loss_cls: 0.289743 loss: 14.983472 eta: 1:03:08 batch_cost: 0.7083 data_cost: 0.0003 ips: 16.9427 images/s\n",
      "[08/13 15:21:03] ppdet.engine INFO: Epoch: [113] [ 0/36] learning_rate: 0.003330 loss_xy: 0.898433 loss_wh: 1.307562 loss_iou: 3.663956 loss_iou_aware: 0.837797 loss_obj: 6.103395 loss_cls: 0.334056 loss: 13.210299 eta: 1:02:57 batch_cost: 0.6694 data_cost: 0.0002 ips: 17.9272 images/s\n",
      "[08/13 15:21:17] ppdet.engine INFO: Epoch: [113] [20/36] learning_rate: 0.003330 loss_xy: 0.842730 loss_wh: 1.188806 loss_iou: 3.619964 loss_iou_aware: 0.834957 loss_obj: 5.771049 loss_cls: 0.228582 loss: 12.035793 eta: 1:02:42 batch_cost: 0.6102 data_cost: 0.0004 ips: 19.6659 images/s\n",
      "[08/13 15:21:31] ppdet.engine INFO: Epoch: [114] [ 0/36] learning_rate: 0.003330 loss_xy: 0.941095 loss_wh: 1.648314 loss_iou: 4.466238 loss_iou_aware: 0.981654 loss_obj: 6.503000 loss_cls: 0.254613 loss: 14.958340 eta: 1:02:32 batch_cost: 0.6957 data_cost: 0.0293 ips: 17.2479 images/s\n",
      "[08/13 15:21:48] ppdet.engine INFO: Epoch: [114] [20/36] learning_rate: 0.003330 loss_xy: 0.965497 loss_wh: 1.164416 loss_iou: 3.694537 loss_iou_aware: 0.859826 loss_obj: 5.954839 loss_cls: 0.279675 loss: 12.774176 eta: 1:02:20 batch_cost: 0.7404 data_cost: 0.0003 ips: 16.2085 images/s\n",
      "[08/13 15:22:02] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:22:02] ppdet.engine INFO: Epoch: [115] [ 0/36] learning_rate: 0.003330 loss_xy: 0.924925 loss_wh: 1.306968 loss_iou: 4.020796 loss_iou_aware: 0.920792 loss_obj: 6.550660 loss_cls: 0.285709 loss: 13.340132 eta: 1:02:09 batch_cost: 0.6470 data_cost: 0.0003 ips: 18.5467 images/s\n",
      "[08/13 15:22:17] ppdet.engine INFO: Epoch: [115] [20/36] learning_rate: 0.003330 loss_xy: 0.963179 loss_wh: 1.378414 loss_iou: 4.059206 loss_iou_aware: 0.861444 loss_obj: 6.370678 loss_cls: 0.287671 loss: 13.990061 eta: 1:01:53 batch_cost: 0.5864 data_cost: 0.0003 ips: 20.4628 images/s\n",
      "[08/13 15:22:29] ppdet.engine INFO: Epoch: [116] [ 0/36] learning_rate: 0.003330 loss_xy: 0.838925 loss_wh: 1.299648 loss_iou: 3.591033 loss_iou_aware: 0.799088 loss_obj: 5.905736 loss_cls: 0.317859 loss: 12.609193 eta: 1:01:41 batch_cost: 0.5689 data_cost: 0.0133 ips: 21.0918 images/s\n",
      "[08/13 15:22:47] ppdet.engine INFO: Epoch: [116] [20/36] learning_rate: 0.003330 loss_xy: 0.779134 loss_wh: 1.164890 loss_iou: 3.343484 loss_iou_aware: 0.739424 loss_obj: 5.600821 loss_cls: 0.190098 loss: 11.843779 eta: 1:01:29 batch_cost: 0.7303 data_cost: 0.0003 ips: 16.4316 images/s\n",
      "[08/13 15:23:02] ppdet.engine INFO: Epoch: [117] [ 0/36] learning_rate: 0.003330 loss_xy: 0.802120 loss_wh: 1.113551 loss_iou: 3.213505 loss_iou_aware: 0.759304 loss_obj: 5.530984 loss_cls: 0.216398 loss: 11.456791 eta: 1:01:21 batch_cost: 0.7529 data_cost: 0.0541 ips: 15.9392 images/s\n",
      "[08/13 15:23:18] ppdet.engine INFO: Epoch: [117] [20/36] learning_rate: 0.003330 loss_xy: 0.870705 loss_wh: 1.338890 loss_iou: 3.759510 loss_iou_aware: 0.883741 loss_obj: 6.379292 loss_cls: 0.236781 loss: 13.509361 eta: 1:01:07 batch_cost: 0.6670 data_cost: 0.0003 ips: 17.9914 images/s\n",
      "[08/13 15:23:34] ppdet.engine INFO: Epoch: [118] [ 0/36] learning_rate: 0.003330 loss_xy: 0.892731 loss_wh: 1.255571 loss_iou: 3.600734 loss_iou_aware: 0.802504 loss_obj: 5.936172 loss_cls: 0.264717 loss: 12.475360 eta: 1:00:59 batch_cost: 0.7461 data_cost: 0.0395 ips: 16.0830 images/s\n",
      "[08/13 15:23:48] ppdet.engine INFO: Epoch: [118] [20/36] learning_rate: 0.003330 loss_xy: 0.942757 loss_wh: 1.135362 loss_iou: 3.654467 loss_iou_aware: 0.861298 loss_obj: 5.857193 loss_cls: 0.253467 loss: 12.893337 eta: 1:00:45 batch_cost: 0.6548 data_cost: 0.0002 ips: 18.3256 images/s\n",
      "[08/13 15:24:01] ppdet.engine INFO: Epoch: [119] [ 0/36] learning_rate: 0.003330 loss_xy: 0.929735 loss_wh: 1.367185 loss_iou: 4.212096 loss_iou_aware: 0.911982 loss_obj: 5.786631 loss_cls: 0.253323 loss: 13.334594 eta: 1:00:34 batch_cost: 0.6311 data_cost: 0.0286 ips: 19.0131 images/s\n",
      "[08/13 15:24:18] ppdet.engine INFO: Epoch: [119] [20/36] learning_rate: 0.003330 loss_xy: 0.944802 loss_wh: 1.249965 loss_iou: 3.726499 loss_iou_aware: 0.833173 loss_obj: 6.042867 loss_cls: 0.247043 loss: 12.992540 eta: 1:00:21 batch_cost: 0.6624 data_cost: 0.0003 ips: 18.1165 images/s\n",
      "[08/13 15:24:32] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:24:33] ppdet.engine INFO: Epoch: [120] [ 0/36] learning_rate: 0.003330 loss_xy: 0.853465 loss_wh: 1.051012 loss_iou: 3.281043 loss_iou_aware: 0.770792 loss_obj: 5.290178 loss_cls: 0.226667 loss: 11.420917 eta: 1:00:09 batch_cost: 0.6068 data_cost: 0.0003 ips: 19.7755 images/s\n",
      "[08/13 15:24:48] ppdet.engine INFO: Epoch: [120] [20/36] learning_rate: 0.003330 loss_xy: 0.899724 loss_wh: 1.249241 loss_iou: 3.580870 loss_iou_aware: 0.822287 loss_obj: 5.914848 loss_cls: 0.217750 loss: 12.850006 eta: 0:59:55 batch_cost: 0.6326 data_cost: 0.0003 ips: 18.9707 images/s\n",
      "[08/13 15:25:04] ppdet.engine INFO: Epoch: [121] [ 0/36] learning_rate: 0.003330 loss_xy: 0.914147 loss_wh: 1.323237 loss_iou: 3.665820 loss_iou_aware: 0.831457 loss_obj: 6.376629 loss_cls: 0.222825 loss: 13.111984 eta: 0:59:46 batch_cost: 0.7386 data_cost: 0.0415 ips: 16.2470 images/s\n",
      "[08/13 15:25:20] ppdet.engine INFO: Epoch: [121] [20/36] learning_rate: 0.003330 loss_xy: 0.917131 loss_wh: 1.350483 loss_iou: 3.858541 loss_iou_aware: 0.811971 loss_obj: 5.856627 loss_cls: 0.245892 loss: 12.845490 eta: 0:59:33 batch_cost: 0.6885 data_cost: 0.0002 ips: 17.4291 images/s\n",
      "[08/13 15:25:37] ppdet.engine INFO: Epoch: [122] [ 0/36] learning_rate: 0.003330 loss_xy: 0.884822 loss_wh: 1.154603 loss_iou: 3.664937 loss_iou_aware: 0.831494 loss_obj: 5.591569 loss_cls: 0.262392 loss: 12.773956 eta: 0:59:25 batch_cost: 0.8037 data_cost: 0.0491 ips: 14.9301 images/s\n",
      "[08/13 15:25:53] ppdet.engine INFO: Epoch: [122] [20/36] learning_rate: 0.003330 loss_xy: 0.919114 loss_wh: 1.300984 loss_iou: 3.745998 loss_iou_aware: 0.837037 loss_obj: 5.743830 loss_cls: 0.252976 loss: 12.638191 eta: 0:59:12 batch_cost: 0.6738 data_cost: 0.0003 ips: 17.8090 images/s\n",
      "[08/13 15:26:07] ppdet.engine INFO: Epoch: [123] [ 0/36] learning_rate: 0.003330 loss_xy: 0.906270 loss_wh: 1.476226 loss_iou: 3.937997 loss_iou_aware: 0.844663 loss_obj: 6.223977 loss_cls: 0.283893 loss: 13.135553 eta: 0:59:02 batch_cost: 0.7096 data_cost: 0.0396 ips: 16.9107 images/s\n",
      "[08/13 15:26:25] ppdet.engine INFO: Epoch: [123] [20/36] learning_rate: 0.003330 loss_xy: 0.902201 loss_wh: 1.495895 loss_iou: 3.872357 loss_iou_aware: 0.815526 loss_obj: 6.282892 loss_cls: 0.278695 loss: 13.705802 eta: 0:58:51 batch_cost: 0.7955 data_cost: 0.0333 ips: 15.0847 images/s\n",
      "[08/13 15:26:39] ppdet.engine INFO: Epoch: [124] [ 0/36] learning_rate: 0.003330 loss_xy: 0.883756 loss_wh: 1.460083 loss_iou: 4.127840 loss_iou_aware: 0.874375 loss_obj: 6.089491 loss_cls: 0.356660 loss: 14.039162 eta: 0:58:41 batch_cost: 0.6642 data_cost: 0.0256 ips: 18.0665 images/s\n",
      "[08/13 15:26:53] ppdet.engine INFO: Epoch: [124] [20/36] learning_rate: 0.003330 loss_xy: 0.895445 loss_wh: 1.189384 loss_iou: 3.704021 loss_iou_aware: 0.800042 loss_obj: 6.151185 loss_cls: 0.257073 loss: 12.976988 eta: 0:58:24 batch_cost: 0.5431 data_cost: 0.0003 ips: 22.0957 images/s\n",
      "[08/13 15:27:07] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:27:08] ppdet.engine INFO: Epoch: [125] [ 0/36] learning_rate: 0.003330 loss_xy: 0.891592 loss_wh: 1.229781 loss_iou: 3.639971 loss_iou_aware: 0.827845 loss_obj: 6.014811 loss_cls: 0.242279 loss: 13.128412 eta: 0:58:13 batch_cost: 0.5816 data_cost: 0.0002 ips: 20.6344 images/s\n",
      "[08/13 15:27:24] ppdet.engine INFO: Epoch: [125] [20/36] learning_rate: 0.003330 loss_xy: 0.878630 loss_wh: 1.342631 loss_iou: 3.779661 loss_iou_aware: 0.849190 loss_obj: 6.032783 loss_cls: 0.279872 loss: 12.601473 eta: 0:58:00 batch_cost: 0.6910 data_cost: 0.0003 ips: 17.3657 images/s\n",
      "[08/13 15:27:39] ppdet.engine INFO: Epoch: [126] [ 0/36] learning_rate: 0.003330 loss_xy: 0.863837 loss_wh: 1.266054 loss_iou: 3.428215 loss_iou_aware: 0.786483 loss_obj: 5.483774 loss_cls: 0.267523 loss: 11.984234 eta: 0:57:50 batch_cost: 0.6958 data_cost: 0.0003 ips: 17.2474 images/s\n",
      "[08/13 15:27:56] ppdet.engine INFO: Epoch: [126] [20/36] learning_rate: 0.003330 loss_xy: 0.857831 loss_wh: 1.223725 loss_iou: 3.483286 loss_iou_aware: 0.775684 loss_obj: 5.496888 loss_cls: 0.271624 loss: 12.457949 eta: 0:57:37 batch_cost: 0.7108 data_cost: 0.0456 ips: 16.8821 images/s\n",
      "[08/13 15:28:10] ppdet.engine INFO: Epoch: [127] [ 0/36] learning_rate: 0.003330 loss_xy: 0.798460 loss_wh: 1.330400 loss_iou: 3.435646 loss_iou_aware: 0.798146 loss_obj: 5.617247 loss_cls: 0.247841 loss: 12.309802 eta: 0:57:27 batch_cost: 0.6890 data_cost: 0.0412 ips: 17.4158 images/s\n",
      "[08/13 15:28:26] ppdet.engine INFO: Epoch: [127] [20/36] learning_rate: 0.003330 loss_xy: 0.832299 loss_wh: 1.278930 loss_iou: 3.591481 loss_iou_aware: 0.803164 loss_obj: 5.191137 loss_cls: 0.231460 loss: 12.421169 eta: 0:57:15 batch_cost: 0.6972 data_cost: 0.1175 ips: 17.2108 images/s\n",
      "[08/13 15:28:39] ppdet.engine INFO: Epoch: [128] [ 0/36] learning_rate: 0.003330 loss_xy: 0.979383 loss_wh: 1.358071 loss_iou: 4.079023 loss_iou_aware: 0.893200 loss_obj: 6.305522 loss_cls: 0.263290 loss: 13.641893 eta: 0:57:03 batch_cost: 0.7118 data_cost: 0.0729 ips: 16.8591 images/s\n",
      "[08/13 15:28:56] ppdet.engine INFO: Epoch: [128] [20/36] learning_rate: 0.003330 loss_xy: 0.856589 loss_wh: 1.301401 loss_iou: 3.799586 loss_iou_aware: 0.837494 loss_obj: 5.827284 loss_cls: 0.250105 loss: 13.034122 eta: 0:56:50 batch_cost: 0.6681 data_cost: 0.0002 ips: 17.9615 images/s\n",
      "[08/13 15:29:11] ppdet.engine INFO: Epoch: [129] [ 0/36] learning_rate: 0.003330 loss_xy: 0.839439 loss_wh: 1.039422 loss_iou: 3.214324 loss_iou_aware: 0.778082 loss_obj: 5.869622 loss_cls: 0.227419 loss: 11.629303 eta: 0:56:40 batch_cost: 0.6956 data_cost: 0.0522 ips: 17.2505 images/s\n",
      "[08/13 15:29:31] ppdet.engine INFO: Epoch: [129] [20/36] learning_rate: 0.003330 loss_xy: 0.848036 loss_wh: 1.168625 loss_iou: 3.696955 loss_iou_aware: 0.863119 loss_obj: 5.936506 loss_cls: 0.217150 loss: 12.604065 eta: 0:56:31 batch_cost: 0.8907 data_cost: 0.0003 ips: 13.4720 images/s\n",
      "[08/13 15:29:46] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
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      "[08/13 15:32:02] ppdet.engine INFO: Epoch: [134] [20/36] learning_rate: 0.003330 loss_xy: 0.899967 loss_wh: 1.140130 loss_iou: 3.479068 loss_iou_aware: 0.826297 loss_obj: 5.617134 loss_cls: 0.215324 loss: 12.129511 eta: 0:54:31 batch_cost: 0.6281 data_cost: 0.0003 ips: 19.1042 images/s\n",
      "[08/13 15:32:16] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:32:17] ppdet.engine INFO: Epoch: [135] [ 0/36] learning_rate: 0.003330 loss_xy: 0.902573 loss_wh: 1.350611 loss_iou: 3.941693 loss_iou_aware: 0.860670 loss_obj: 5.472274 loss_cls: 0.210041 loss: 12.724300 eta: 0:54:20 batch_cost: 0.6975 data_cost: 0.0002 ips: 17.2039 images/s\n",
      "[08/13 15:32:33] ppdet.engine INFO: Epoch: [135] [20/36] learning_rate: 0.003330 loss_xy: 0.935844 loss_wh: 1.181678 loss_iou: 3.819082 loss_iou_aware: 0.882542 loss_obj: 5.830783 loss_cls: 0.241878 loss: 13.186972 eta: 0:54:07 batch_cost: 0.6679 data_cost: 0.0443 ips: 17.9664 images/s\n",
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      "[08/13 15:33:04] ppdet.engine INFO: Epoch: [136] [20/36] learning_rate: 0.003330 loss_xy: 0.844818 loss_wh: 1.249240 loss_iou: 3.416674 loss_iou_aware: 0.737658 loss_obj: 5.347393 loss_cls: 0.203184 loss: 11.614065 eta: 0:53:44 batch_cost: 0.6507 data_cost: 0.0005 ips: 18.4406 images/s\n",
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      "[08/13 15:34:01] ppdet.engine INFO: Epoch: [138] [20/36] learning_rate: 0.003330 loss_xy: 0.782356 loss_wh: 1.240055 loss_iou: 3.510039 loss_iou_aware: 0.805124 loss_obj: 5.106215 loss_cls: 0.206448 loss: 11.806146 eta: 0:52:52 batch_cost: 0.6326 data_cost: 0.0003 ips: 18.9693 images/s\n",
      "[08/13 15:34:16] ppdet.engine INFO: Epoch: [139] [ 0/36] learning_rate: 0.003330 loss_xy: 0.845419 loss_wh: 1.232361 loss_iou: 3.875767 loss_iou_aware: 0.877010 loss_obj: 5.811839 loss_cls: 0.224048 loss: 12.909649 eta: 0:52:41 batch_cost: 0.6388 data_cost: 0.0042 ips: 18.7866 images/s\n",
      "[08/13 15:34:32] ppdet.engine INFO: Epoch: [139] [20/36] learning_rate: 0.003330 loss_xy: 0.811902 loss_wh: 1.090638 loss_iou: 3.396708 loss_iou_aware: 0.794573 loss_obj: 5.467115 loss_cls: 0.200985 loss: 11.750007 eta: 0:52:27 batch_cost: 0.6144 data_cost: 0.0007 ips: 19.5315 images/s\n",
      "[08/13 15:34:47] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:34:49] ppdet.engine INFO: Epoch: [140] [ 0/36] learning_rate: 0.003330 loss_xy: 0.895925 loss_wh: 1.035056 loss_iou: 3.179053 loss_iou_aware: 0.752703 loss_obj: 5.545058 loss_cls: 0.193818 loss: 11.615410 eta: 0:52:17 batch_cost: 0.6795 data_cost: 0.0003 ips: 17.6611 images/s\n",
      "[08/13 15:35:05] ppdet.engine INFO: Epoch: [140] [20/36] learning_rate: 0.003330 loss_xy: 0.797877 loss_wh: 1.206143 loss_iou: 3.476662 loss_iou_aware: 0.794245 loss_obj: 5.035198 loss_cls: 0.202265 loss: 11.929987 eta: 0:52:04 batch_cost: 0.7023 data_cost: 0.0003 ips: 17.0879 images/s\n",
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      "[08/13 15:35:48] ppdet.engine INFO: Epoch: [142] [ 0/36] learning_rate: 0.003330 loss_xy: 0.834960 loss_wh: 1.093031 loss_iou: 3.524392 loss_iou_aware: 0.861313 loss_obj: 5.463877 loss_cls: 0.164918 loss: 12.301584 eta: 0:51:29 batch_cost: 0.6384 data_cost: 0.0003 ips: 18.7981 images/s\n",
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      "[08/13 15:36:36] ppdet.engine INFO: Epoch: [143] [20/36] learning_rate: 0.003330 loss_xy: 0.899663 loss_wh: 0.996479 loss_iou: 3.367517 loss_iou_aware: 0.809560 loss_obj: 5.454977 loss_cls: 0.208589 loss: 11.729747 eta: 0:50:54 batch_cost: 0.6439 data_cost: 0.0002 ips: 18.6370 images/s\n",
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      "[08/13 15:37:04] ppdet.engine INFO: Epoch: [144] [20/36] learning_rate: 0.003330 loss_xy: 0.898977 loss_wh: 1.132271 loss_iou: 3.501510 loss_iou_aware: 0.829655 loss_obj: 5.915290 loss_cls: 0.203480 loss: 12.477387 eta: 0:50:28 batch_cost: 0.5859 data_cost: 0.0002 ips: 20.4806 images/s\n",
      "[08/13 15:37:17] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:37:17] ppdet.engine INFO: Epoch: [145] [ 0/36] learning_rate: 0.003330 loss_xy: 0.851078 loss_wh: 1.099424 loss_iou: 3.405771 loss_iou_aware: 0.808498 loss_obj: 5.453017 loss_cls: 0.176191 loss: 11.780172 eta: 0:50:16 batch_cost: 0.5782 data_cost: 0.0002 ips: 20.7547 images/s\n",
      "[08/13 15:37:34] ppdet.engine INFO: Epoch: [145] [20/36] learning_rate: 0.003330 loss_xy: 0.806349 loss_wh: 1.194573 loss_iou: 3.428309 loss_iou_aware: 0.796797 loss_obj: 5.726091 loss_cls: 0.217789 loss: 12.144201 eta: 0:50:03 batch_cost: 0.7144 data_cost: 0.0003 ips: 16.7962 images/s\n",
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      "[08/13 15:38:16] ppdet.engine INFO: Epoch: [147] [ 0/36] learning_rate: 0.003330 loss_xy: 0.889028 loss_wh: 1.131493 loss_iou: 3.748275 loss_iou_aware: 0.891163 loss_obj: 5.587909 loss_cls: 0.199176 loss: 12.740297 eta: 0:49:27 batch_cost: 0.6185 data_cost: 0.0210 ips: 19.4005 images/s\n",
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      "[08/13 15:39:00] ppdet.engine INFO: Epoch: [148] [20/36] learning_rate: 0.003330 loss_xy: 0.884005 loss_wh: 1.160546 loss_iou: 3.462816 loss_iou_aware: 0.893645 loss_obj: 5.674196 loss_cls: 0.285539 loss: 12.403071 eta: 0:48:49 batch_cost: 0.7033 data_cost: 0.0003 ips: 17.0615 images/s\n",
      "[08/13 15:39:15] ppdet.engine INFO: Epoch: [149] [ 0/36] learning_rate: 0.003330 loss_xy: 0.893275 loss_wh: 1.214885 loss_iou: 3.624082 loss_iou_aware: 0.828380 loss_obj: 5.629044 loss_cls: 0.221621 loss: 12.410751 eta: 0:48:39 batch_cost: 0.7139 data_cost: 0.0154 ips: 16.8092 images/s\n",
      "[08/13 15:39:29] ppdet.engine INFO: Epoch: [149] [20/36] learning_rate: 0.003330 loss_xy: 0.825889 loss_wh: 1.086025 loss_iou: 3.561574 loss_iou_aware: 0.841029 loss_obj: 5.515184 loss_cls: 0.229716 loss: 12.363266 eta: 0:48:24 batch_cost: 0.6076 data_cost: 0.0003 ips: 19.7485 images/s\n",
      "[08/13 15:39:42] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:39:44] ppdet.engine INFO: Epoch: [150] [ 0/36] learning_rate: 0.003330 loss_xy: 0.842850 loss_wh: 1.216536 loss_iou: 3.472142 loss_iou_aware: 0.846117 loss_obj: 5.661654 loss_cls: 0.194153 loss: 12.718791 eta: 0:48:13 batch_cost: 0.6053 data_cost: 0.0002 ips: 19.8250 images/s\n",
      "[08/13 15:39:59] ppdet.engine INFO: Epoch: [150] [20/36] learning_rate: 0.003330 loss_xy: 0.795737 loss_wh: 1.139382 loss_iou: 3.460135 loss_iou_aware: 0.779784 loss_obj: 5.153495 loss_cls: 0.202658 loss: 12.026201 eta: 0:47:59 batch_cost: 0.6657 data_cost: 0.0003 ips: 18.0261 images/s\n",
      "[08/13 15:40:15] ppdet.engine INFO: Epoch: [151] [ 0/36] learning_rate: 0.003330 loss_xy: 0.853180 loss_wh: 1.058203 loss_iou: 3.308728 loss_iou_aware: 0.762827 loss_obj: 5.325652 loss_cls: 0.263728 loss: 11.599001 eta: 0:47:50 batch_cost: 0.8189 data_cost: 0.0669 ips: 14.6540 images/s\n",
      "[08/13 15:40:31] ppdet.engine INFO: Epoch: [151] [20/36] learning_rate: 0.003330 loss_xy: 0.789267 loss_wh: 1.057026 loss_iou: 3.279095 loss_iou_aware: 0.789794 loss_obj: 5.103501 loss_cls: 0.235986 loss: 11.840813 eta: 0:47:38 batch_cost: 0.6996 data_cost: 0.0002 ips: 17.1517 images/s\n",
      "[08/13 15:40:44] ppdet.engine INFO: Epoch: [152] [ 0/36] learning_rate: 0.003330 loss_xy: 0.771766 loss_wh: 1.203387 loss_iou: 3.188189 loss_iou_aware: 0.735745 loss_obj: 5.115586 loss_cls: 0.196439 loss: 11.337357 eta: 0:47:25 batch_cost: 0.6133 data_cost: 0.0053 ips: 19.5675 images/s\n",
      "[08/13 15:41:00] ppdet.engine INFO: Epoch: [152] [20/36] learning_rate: 0.003330 loss_xy: 0.805003 loss_wh: 1.024418 loss_iou: 3.143456 loss_iou_aware: 0.770479 loss_obj: 4.908195 loss_cls: 0.166986 loss: 11.476023 eta: 0:47:12 batch_cost: 0.6652 data_cost: 0.0401 ips: 18.0394 images/s\n",
      "[08/13 15:41:15] ppdet.engine INFO: Epoch: [153] [ 0/36] learning_rate: 0.003330 loss_xy: 0.847809 loss_wh: 0.990232 loss_iou: 3.443557 loss_iou_aware: 0.817673 loss_obj: 5.312892 loss_cls: 0.177048 loss: 11.604120 eta: 0:47:03 batch_cost: 0.7577 data_cost: 0.0627 ips: 15.8374 images/s\n",
      "[08/13 15:41:31] ppdet.engine INFO: Epoch: [153] [20/36] learning_rate: 0.003330 loss_xy: 0.791034 loss_wh: 0.987064 loss_iou: 3.168248 loss_iou_aware: 0.782125 loss_obj: 5.048193 loss_cls: 0.193187 loss: 11.342207 eta: 0:46:49 batch_cost: 0.6569 data_cost: 0.0003 ips: 18.2671 images/s\n",
      "[08/13 15:41:45] ppdet.engine INFO: Epoch: [154] [ 0/36] learning_rate: 0.003330 loss_xy: 0.894506 loss_wh: 1.070862 loss_iou: 3.504902 loss_iou_aware: 0.848043 loss_obj: 5.819612 loss_cls: 0.206865 loss: 12.696760 eta: 0:46:39 batch_cost: 0.7569 data_cost: 0.0346 ips: 15.8538 images/s\n",
      "[08/13 15:42:00] ppdet.engine INFO: Epoch: [154] [20/36] learning_rate: 0.003330 loss_xy: 0.814878 loss_wh: 1.156465 loss_iou: 3.789536 loss_iou_aware: 0.838381 loss_obj: 5.565661 loss_cls: 0.225383 loss: 12.106764 eta: 0:46:25 batch_cost: 0.6454 data_cost: 0.0003 ips: 18.5923 images/s\n",
      "[08/13 15:42:13] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:42:13] ppdet.engine INFO: Epoch: [155] [ 0/36] learning_rate: 0.003330 loss_xy: 0.819925 loss_wh: 1.145174 loss_iou: 3.816017 loss_iou_aware: 0.897894 loss_obj: 5.320086 loss_cls: 0.236429 loss: 12.475952 eta: 0:46:13 batch_cost: 0.4976 data_cost: 0.0003 ips: 24.1152 images/s\n",
      "[08/13 15:42:28] ppdet.engine INFO: Epoch: [155] [20/36] learning_rate: 0.003330 loss_xy: 0.776222 loss_wh: 0.970062 loss_iou: 3.214705 loss_iou_aware: 0.790146 loss_obj: 5.032767 loss_cls: 0.182563 loss: 11.335116 eta: 0:45:59 batch_cost: 0.6378 data_cost: 0.0003 ips: 18.8142 images/s\n",
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      "[08/13 15:43:01] ppdet.engine INFO: Epoch: [156] [20/36] learning_rate: 0.003330 loss_xy: 0.886093 loss_wh: 1.149523 loss_iou: 3.636240 loss_iou_aware: 0.868565 loss_obj: 5.645865 loss_cls: 0.244717 loss: 12.824361 eta: 0:45:36 batch_cost: 0.7420 data_cost: 0.0003 ips: 16.1734 images/s\n",
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      "[08/13 15:43:31] ppdet.engine INFO: Epoch: [157] [20/36] learning_rate: 0.003330 loss_xy: 0.820715 loss_wh: 1.142869 loss_iou: 3.513307 loss_iou_aware: 0.846909 loss_obj: 5.540470 loss_cls: 0.252328 loss: 12.235979 eta: 0:45:12 batch_cost: 0.6264 data_cost: 0.0003 ips: 19.1563 images/s\n",
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      "[08/13 15:44:48] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:44:48] ppdet.engine INFO: Epoch: [160] [ 0/36] learning_rate: 0.003330 loss_xy: 0.839068 loss_wh: 1.120151 loss_iou: 3.518078 loss_iou_aware: 0.830170 loss_obj: 5.362071 loss_cls: 0.203677 loss: 11.875872 eta: 0:44:14 batch_cost: 0.6134 data_cost: 0.0003 ips: 19.5624 images/s\n",
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      "[08/13 15:47:18] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:47:18] ppdet.engine INFO: Epoch: [165] [ 0/36] learning_rate: 0.003330 loss_xy: 0.839346 loss_wh: 1.036984 loss_iou: 3.467319 loss_iou_aware: 0.821718 loss_obj: 5.308319 loss_cls: 0.187448 loss: 11.369478 eta: 0:42:14 batch_cost: 0.6307 data_cost: 0.0002 ips: 19.0279 images/s\n",
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      "[08/13 15:47:48] ppdet.engine INFO: Epoch: [166] [ 0/36] learning_rate: 0.003330 loss_xy: 0.875215 loss_wh: 1.039188 loss_iou: 3.267340 loss_iou_aware: 0.793327 loss_obj: 5.098932 loss_cls: 0.173898 loss: 11.232464 eta: 0:41:50 batch_cost: 0.7294 data_cost: 0.0006 ips: 16.4528 images/s\n",
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      "[08/13 15:49:06] ppdet.engine INFO: Epoch: [168] [20/36] learning_rate: 0.003330 loss_xy: 0.820363 loss_wh: 0.934419 loss_iou: 3.141858 loss_iou_aware: 0.765642 loss_obj: 5.086412 loss_cls: 0.181839 loss: 11.078606 eta: 0:40:49 batch_cost: 0.7367 data_cost: 0.0003 ips: 16.2888 images/s\n",
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      "[08/13 15:49:49] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:49:50] ppdet.engine INFO: Epoch: [170] [ 0/36] learning_rate: 0.003330 loss_xy: 0.725667 loss_wh: 0.972113 loss_iou: 3.002811 loss_iou_aware: 0.687577 loss_obj: 5.054852 loss_cls: 0.223737 loss: 10.583672 eta: 0:40:13 batch_cost: 0.6220 data_cost: 0.0003 ips: 19.2911 images/s\n",
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      "[08/13 15:50:51] ppdet.engine INFO: Epoch: [172] [ 0/36] learning_rate: 0.003330 loss_xy: 0.809969 loss_wh: 1.154141 loss_iou: 3.344115 loss_iou_aware: 0.809650 loss_obj: 5.256428 loss_cls: 0.227789 loss: 11.935726 eta: 0:39:26 batch_cost: 0.6851 data_cost: 0.0663 ips: 17.5145 images/s\n",
      "[08/13 15:51:07] ppdet.engine INFO: Epoch: [172] [20/36] learning_rate: 0.003330 loss_xy: 0.851928 loss_wh: 1.154662 loss_iou: 3.337030 loss_iou_aware: 0.792867 loss_obj: 5.146661 loss_cls: 0.229320 loss: 11.675404 eta: 0:39:12 batch_cost: 0.6807 data_cost: 0.0003 ips: 17.6301 images/s\n",
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      "[08/13 15:52:20] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
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      "[08/13 15:53:20] ppdet.engine INFO: Epoch: [177] [ 0/36] learning_rate: 0.003330 loss_xy: 0.828690 loss_wh: 1.086696 loss_iou: 3.521318 loss_iou_aware: 0.835750 loss_obj: 5.655789 loss_cls: 0.181008 loss: 12.141758 eta: 0:37:24 batch_cost: 0.6371 data_cost: 0.0219 ips: 18.8365 images/s\n",
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      "[08/13 15:54:04] ppdet.engine INFO: Epoch: [178] [20/36] learning_rate: 0.003330 loss_xy: 0.736240 loss_wh: 0.990671 loss_iou: 3.103857 loss_iou_aware: 0.760577 loss_obj: 5.117707 loss_cls: 0.237617 loss: 10.912374 eta: 0:36:46 batch_cost: 0.6380 data_cost: 0.0003 ips: 18.8087 images/s\n",
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      "[08/13 15:54:36] ppdet.engine INFO: Epoch: [179] [20/36] learning_rate: 0.003330 loss_xy: 0.780809 loss_wh: 0.870646 loss_iou: 2.995843 loss_iou_aware: 0.751483 loss_obj: 5.383887 loss_cls: 0.161146 loss: 11.218547 eta: 0:36:23 batch_cost: 0.7268 data_cost: 0.0003 ips: 16.5118 images/s\n",
      "[08/13 15:54:51] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:54:52] ppdet.engine INFO: Epoch: [180] [ 0/36] learning_rate: 0.003330 loss_xy: 0.755139 loss_wh: 0.977159 loss_iou: 3.153764 loss_iou_aware: 0.756450 loss_obj: 5.089904 loss_cls: 0.140948 loss: 10.600115 eta: 0:36:12 batch_cost: 0.7454 data_cost: 0.0003 ips: 16.0977 images/s\n",
      "[08/13 15:55:12] ppdet.engine INFO: Epoch: [180] [20/36] learning_rate: 0.003330 loss_xy: 0.794478 loss_wh: 1.074392 loss_iou: 3.364500 loss_iou_aware: 0.805378 loss_obj: 4.989511 loss_cls: 0.186553 loss: 10.895658 eta: 0:36:00 batch_cost: 0.7737 data_cost: 0.0003 ips: 15.5090 images/s\n",
      "[08/13 15:55:28] ppdet.engine INFO: Epoch: [181] [ 0/36] learning_rate: 0.003330 loss_xy: 0.745181 loss_wh: 1.058611 loss_iou: 3.367013 loss_iou_aware: 0.815451 loss_obj: 5.067119 loss_cls: 0.209428 loss: 10.822538 eta: 0:35:50 batch_cost: 0.7554 data_cost: 0.0622 ips: 15.8864 images/s\n",
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      "[08/13 15:55:53] ppdet.engine INFO: Epoch: [182] [ 0/36] learning_rate: 0.003330 loss_xy: 0.822025 loss_wh: 1.339594 loss_iou: 3.793708 loss_iou_aware: 0.864230 loss_obj: 5.079192 loss_cls: 0.225564 loss: 12.419724 eta: 0:35:24 batch_cost: 0.5542 data_cost: 0.0284 ips: 21.6539 images/s\n",
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      "[08/13 15:56:23] ppdet.engine INFO: Epoch: [183] [ 0/36] learning_rate: 0.003330 loss_xy: 0.809420 loss_wh: 1.091300 loss_iou: 3.322111 loss_iou_aware: 0.756136 loss_obj: 5.315991 loss_cls: 0.243731 loss: 11.273531 eta: 0:35:00 batch_cost: 0.7431 data_cost: 0.0365 ips: 16.1478 images/s\n",
      "[08/13 15:56:38] ppdet.engine INFO: Epoch: [183] [20/36] learning_rate: 0.003330 loss_xy: 0.891524 loss_wh: 0.891917 loss_iou: 3.388604 loss_iou_aware: 0.813775 loss_obj: 5.351774 loss_cls: 0.206234 loss: 11.161221 eta: 0:34:47 batch_cost: 0.6368 data_cost: 0.0003 ips: 18.8441 images/s\n",
      "[08/13 15:56:52] ppdet.engine INFO: Epoch: [184] [ 0/36] learning_rate: 0.003330 loss_xy: 0.869728 loss_wh: 1.046136 loss_iou: 3.427998 loss_iou_aware: 0.836188 loss_obj: 5.151607 loss_cls: 0.174079 loss: 11.238065 eta: 0:34:36 batch_cost: 0.6599 data_cost: 0.0088 ips: 18.1833 images/s\n",
      "[08/13 15:57:07] ppdet.engine INFO: Epoch: [184] [20/36] learning_rate: 0.003330 loss_xy: 0.785976 loss_wh: 0.942479 loss_iou: 3.197724 loss_iou_aware: 0.806846 loss_obj: 4.896802 loss_cls: 0.196215 loss: 10.904821 eta: 0:34:22 batch_cost: 0.6354 data_cost: 0.0003 ips: 18.8860 images/s\n",
      "[08/13 15:57:21] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:57:23] ppdet.engine INFO: Epoch: [185] [ 0/36] learning_rate: 0.003330 loss_xy: 0.808702 loss_wh: 1.092521 loss_iou: 3.520372 loss_iou_aware: 0.807479 loss_obj: 5.245452 loss_cls: 0.214700 loss: 11.615454 eta: 0:34:12 batch_cost: 0.6680 data_cost: 0.0338 ips: 17.9648 images/s\n",
      "[08/13 15:57:40] ppdet.engine INFO: Epoch: [185] [20/36] learning_rate: 0.003330 loss_xy: 0.832167 loss_wh: 1.229378 loss_iou: 3.815816 loss_iou_aware: 0.914963 loss_obj: 5.447640 loss_cls: 0.237039 loss: 12.396601 eta: 0:33:59 batch_cost: 0.7599 data_cost: 0.0006 ips: 15.7912 images/s\n",
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      "[08/13 15:58:25] ppdet.engine INFO: Epoch: [187] [ 0/36] learning_rate: 0.003330 loss_xy: 0.831200 loss_wh: 1.008052 loss_iou: 2.979146 loss_iou_aware: 0.746678 loss_obj: 4.857815 loss_cls: 0.171457 loss: 10.480467 eta: 0:33:25 batch_cost: 0.6676 data_cost: 0.0003 ips: 17.9740 images/s\n",
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      "[08/13 15:58:57] ppdet.engine INFO: Epoch: [188] [ 0/36] learning_rate: 0.003330 loss_xy: 0.806151 loss_wh: 1.108690 loss_iou: 3.513851 loss_iou_aware: 0.845738 loss_obj: 5.426574 loss_cls: 0.190477 loss: 11.834276 eta: 0:33:02 batch_cost: 0.7186 data_cost: 0.0205 ips: 16.6988 images/s\n",
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      "[08/13 15:59:26] ppdet.engine INFO: Epoch: [189] [ 0/36] learning_rate: 0.003330 loss_xy: 0.823478 loss_wh: 1.104169 loss_iou: 3.520481 loss_iou_aware: 0.792836 loss_obj: 5.041331 loss_cls: 0.153982 loss: 11.684649 eta: 0:32:37 batch_cost: 0.6940 data_cost: 0.0525 ips: 17.2910 images/s\n",
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      "[08/13 15:59:53] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 15:59:54] ppdet.engine INFO: Epoch: [190] [ 0/36] learning_rate: 0.003330 loss_xy: 0.813801 loss_wh: 1.129226 loss_iou: 3.764522 loss_iou_aware: 0.902404 loss_obj: 5.249869 loss_cls: 0.223795 loss: 11.771783 eta: 0:32:11 batch_cost: 0.5299 data_cost: 0.0002 ips: 22.6443 images/s\n",
      "[08/13 16:00:11] ppdet.engine INFO: Epoch: [190] [20/36] learning_rate: 0.003330 loss_xy: 0.791525 loss_wh: 0.953134 loss_iou: 3.109596 loss_iou_aware: 0.738875 loss_obj: 4.901480 loss_cls: 0.205747 loss: 10.694122 eta: 0:31:58 batch_cost: 0.7098 data_cost: 0.0003 ips: 16.9068 images/s\n",
      "[08/13 16:00:23] ppdet.engine INFO: Epoch: [191] [ 0/36] learning_rate: 0.003330 loss_xy: 0.762485 loss_wh: 1.217044 loss_iou: 3.573016 loss_iou_aware: 0.778820 loss_obj: 5.118377 loss_cls: 0.197003 loss: 11.169142 eta: 0:31:47 batch_cost: 0.6596 data_cost: 0.0163 ips: 18.1937 images/s\n",
      "[08/13 16:00:41] ppdet.engine INFO: Epoch: [191] [20/36] learning_rate: 0.003330 loss_xy: 0.793503 loss_wh: 0.991233 loss_iou: 3.291488 loss_iou_aware: 0.776519 loss_obj: 4.862732 loss_cls: 0.249689 loss: 11.318851 eta: 0:31:34 batch_cost: 0.7138 data_cost: 0.0003 ips: 16.8108 images/s\n",
      "[08/13 16:00:53] ppdet.engine INFO: Epoch: [192] [ 0/36] learning_rate: 0.003330 loss_xy: 0.802547 loss_wh: 0.985479 loss_iou: 3.359071 loss_iou_aware: 0.767102 loss_obj: 4.843765 loss_cls: 0.229042 loss: 10.623396 eta: 0:31:23 batch_cost: 0.5987 data_cost: 0.0002 ips: 20.0427 images/s\n",
      "[08/13 16:01:09] ppdet.engine INFO: Epoch: [192] [20/36] learning_rate: 0.003330 loss_xy: 0.782767 loss_wh: 1.059791 loss_iou: 3.545671 loss_iou_aware: 0.847682 loss_obj: 5.150033 loss_cls: 0.196986 loss: 11.465837 eta: 0:31:09 batch_cost: 0.6288 data_cost: 0.0003 ips: 19.0844 images/s\n",
      "[08/13 16:01:21] ppdet.engine INFO: Epoch: [193] [ 0/36] learning_rate: 0.003330 loss_xy: 0.801177 loss_wh: 1.046527 loss_iou: 3.545241 loss_iou_aware: 0.860412 loss_obj: 5.170959 loss_cls: 0.233000 loss: 11.808207 eta: 0:30:58 batch_cost: 0.6109 data_cost: 0.0318 ips: 19.6443 images/s\n",
      "[08/13 16:01:36] ppdet.engine INFO: Epoch: [193] [20/36] learning_rate: 0.003330 loss_xy: 0.764075 loss_wh: 0.922652 loss_iou: 3.313075 loss_iou_aware: 0.814811 loss_obj: 4.955839 loss_cls: 0.204470 loss: 11.253588 eta: 0:30:45 batch_cost: 0.6584 data_cost: 0.0002 ips: 18.2257 images/s\n",
      "[08/13 16:01:53] ppdet.engine INFO: Epoch: [194] [ 0/36] learning_rate: 0.003330 loss_xy: 0.746243 loss_wh: 0.843389 loss_iou: 2.978542 loss_iou_aware: 0.771583 loss_obj: 5.125221 loss_cls: 0.188691 loss: 11.033692 eta: 0:30:35 batch_cost: 0.8235 data_cost: 0.0738 ips: 14.5727 images/s\n",
      "[08/13 16:02:08] ppdet.engine INFO: Epoch: [194] [20/36] learning_rate: 0.003330 loss_xy: 0.746399 loss_wh: 1.083587 loss_iou: 3.395823 loss_iou_aware: 0.789875 loss_obj: 5.282658 loss_cls: 0.178648 loss: 11.154131 eta: 0:30:21 batch_cost: 0.6414 data_cost: 0.0003 ips: 18.7087 images/s\n",
      "[08/13 16:02:22] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:02:23] ppdet.engine INFO: Epoch: [195] [ 0/36] learning_rate: 0.003330 loss_xy: 0.905464 loss_wh: 1.172367 loss_iou: 3.716351 loss_iou_aware: 0.893880 loss_obj: 5.516230 loss_cls: 0.252403 loss: 12.753954 eta: 0:30:10 batch_cost: 0.5915 data_cost: 0.0003 ips: 20.2866 images/s\n",
      "[08/13 16:02:37] ppdet.engine INFO: Epoch: [195] [20/36] learning_rate: 0.003330 loss_xy: 0.791157 loss_wh: 1.001221 loss_iou: 3.485117 loss_iou_aware: 0.838266 loss_obj: 4.871244 loss_cls: 0.244695 loss: 11.207822 eta: 0:29:56 batch_cost: 0.5951 data_cost: 0.0003 ips: 20.1663 images/s\n",
      "[08/13 16:02:49] ppdet.engine INFO: Epoch: [196] [ 0/36] learning_rate: 0.003330 loss_xy: 0.794738 loss_wh: 0.914538 loss_iou: 3.034721 loss_iou_aware: 0.744948 loss_obj: 4.708953 loss_cls: 0.148805 loss: 10.074481 eta: 0:29:45 batch_cost: 0.5736 data_cost: 0.0002 ips: 20.9219 images/s\n",
      "[08/13 16:03:05] ppdet.engine INFO: Epoch: [196] [20/36] learning_rate: 0.003330 loss_xy: 0.781508 loss_wh: 1.003123 loss_iou: 3.292841 loss_iou_aware: 0.810694 loss_obj: 5.332958 loss_cls: 0.181043 loss: 11.050253 eta: 0:29:31 batch_cost: 0.6496 data_cost: 0.0003 ips: 18.4731 images/s\n",
      "[08/13 16:03:19] ppdet.engine INFO: Epoch: [197] [ 0/36] learning_rate: 0.003330 loss_xy: 0.837344 loss_wh: 1.053985 loss_iou: 3.512932 loss_iou_aware: 0.807280 loss_obj: 5.222226 loss_cls: 0.189367 loss: 11.562074 eta: 0:29:21 batch_cost: 0.7006 data_cost: 0.0158 ips: 17.1282 images/s\n",
      "[08/13 16:03:35] ppdet.engine INFO: Epoch: [197] [20/36] learning_rate: 0.003330 loss_xy: 0.797302 loss_wh: 0.965942 loss_iou: 3.050727 loss_iou_aware: 0.762035 loss_obj: 5.358052 loss_cls: 0.169594 loss: 11.238215 eta: 0:29:08 batch_cost: 0.6753 data_cost: 0.0003 ips: 17.7698 images/s\n",
      "[08/13 16:03:51] ppdet.engine INFO: Epoch: [198] [ 0/36] learning_rate: 0.003330 loss_xy: 0.789738 loss_wh: 0.906754 loss_iou: 3.034360 loss_iou_aware: 0.734889 loss_obj: 5.312071 loss_cls: 0.168525 loss: 10.514019 eta: 0:28:58 batch_cost: 0.7703 data_cost: 0.0080 ips: 15.5784 images/s\n",
      "[08/13 16:04:06] ppdet.engine INFO: Epoch: [198] [20/36] learning_rate: 0.003330 loss_xy: 0.762659 loss_wh: 1.024084 loss_iou: 3.336358 loss_iou_aware: 0.791367 loss_obj: 4.649101 loss_cls: 0.192847 loss: 11.118780 eta: 0:28:44 batch_cost: 0.6530 data_cost: 0.0462 ips: 18.3769 images/s\n",
      "[08/13 16:04:19] ppdet.engine INFO: Epoch: [199] [ 0/36] learning_rate: 0.003330 loss_xy: 0.799940 loss_wh: 1.022353 loss_iou: 3.301740 loss_iou_aware: 0.790946 loss_obj: 5.080086 loss_cls: 0.212463 loss: 11.334337 eta: 0:28:33 batch_cost: 0.6291 data_cost: 0.0604 ips: 19.0742 images/s\n",
      "[08/13 16:04:33] ppdet.engine INFO: Epoch: [199] [20/36] learning_rate: 0.003330 loss_xy: 0.791514 loss_wh: 1.008055 loss_iou: 3.383620 loss_iou_aware: 0.817372 loss_obj: 4.931529 loss_cls: 0.181499 loss: 10.808737 eta: 0:28:19 batch_cost: 0.5789 data_cost: 0.0003 ips: 20.7296 images/s\n",
      "[08/13 16:04:46] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:04:46] ppdet.engine INFO: Epoch: [200] [ 0/36] learning_rate: 0.003330 loss_xy: 0.820250 loss_wh: 1.106196 loss_iou: 3.546740 loss_iou_aware: 0.826991 loss_obj: 5.075405 loss_cls: 0.169630 loss: 11.191989 eta: 0:28:08 batch_cost: 0.5305 data_cost: 0.0002 ips: 22.6189 images/s\n",
      "[08/13 16:05:04] ppdet.engine INFO: Epoch: [200] [20/36] learning_rate: 0.003330 loss_xy: 0.788218 loss_wh: 0.880822 loss_iou: 3.080590 loss_iou_aware: 0.777683 loss_obj: 4.825757 loss_cls: 0.144637 loss: 10.497519 eta: 0:27:55 batch_cost: 0.7363 data_cost: 0.0003 ips: 16.2982 images/s\n",
      "[08/13 16:05:18] ppdet.engine INFO: Epoch: [201] [ 0/36] learning_rate: 0.003330 loss_xy: 0.768569 loss_wh: 0.894703 loss_iou: 3.069045 loss_iou_aware: 0.721188 loss_obj: 4.845383 loss_cls: 0.129545 loss: 10.508593 eta: 0:27:44 batch_cost: 0.6887 data_cost: 0.0064 ips: 17.4248 images/s\n",
      "[08/13 16:05:35] ppdet.engine INFO: Epoch: [201] [20/36] learning_rate: 0.003330 loss_xy: 0.860863 loss_wh: 1.147864 loss_iou: 3.487044 loss_iou_aware: 0.815925 loss_obj: 5.620309 loss_cls: 0.191845 loss: 12.137835 eta: 0:27:31 batch_cost: 0.7479 data_cost: 0.0003 ips: 16.0459 images/s\n",
      "[08/13 16:05:50] ppdet.engine INFO: Epoch: [202] [ 0/36] learning_rate: 0.003330 loss_xy: 0.753484 loss_wh: 0.975428 loss_iou: 3.104340 loss_iou_aware: 0.744087 loss_obj: 4.802424 loss_cls: 0.221000 loss: 10.644324 eta: 0:27:21 batch_cost: 0.7358 data_cost: 0.0701 ips: 16.3091 images/s\n",
      "[08/13 16:06:05] ppdet.engine INFO: Epoch: [202] [20/36] learning_rate: 0.003330 loss_xy: 0.807526 loss_wh: 1.079655 loss_iou: 3.782866 loss_iou_aware: 0.861917 loss_obj: 5.409093 loss_cls: 0.196503 loss: 12.166206 eta: 0:27:07 batch_cost: 0.6030 data_cost: 0.0003 ips: 19.9005 images/s\n",
      "[08/13 16:06:20] ppdet.engine INFO: Epoch: [203] [ 0/36] learning_rate: 0.003330 loss_xy: 0.874295 loss_wh: 1.256628 loss_iou: 3.901046 loss_iou_aware: 0.929879 loss_obj: 5.454694 loss_cls: 0.194701 loss: 12.896531 eta: 0:26:57 batch_cost: 0.7297 data_cost: 0.0679 ips: 16.4451 images/s\n",
      "[08/13 16:06:35] ppdet.engine INFO: Epoch: [203] [20/36] learning_rate: 0.003330 loss_xy: 0.768607 loss_wh: 1.163467 loss_iou: 3.565673 loss_iou_aware: 0.829249 loss_obj: 4.815810 loss_cls: 0.238663 loss: 11.107272 eta: 0:26:43 batch_cost: 0.6509 data_cost: 0.0003 ips: 18.4355 images/s\n",
      "[08/13 16:06:50] ppdet.engine INFO: Epoch: [204] [ 0/36] learning_rate: 0.003330 loss_xy: 0.788174 loss_wh: 1.210374 loss_iou: 3.673694 loss_iou_aware: 0.858535 loss_obj: 4.830091 loss_cls: 0.259665 loss: 11.377452 eta: 0:26:33 batch_cost: 0.7009 data_cost: 0.0115 ips: 17.1205 images/s\n",
      "[08/13 16:07:10] ppdet.engine INFO: Epoch: [204] [20/36] learning_rate: 0.003330 loss_xy: 0.853624 loss_wh: 1.066356 loss_iou: 3.555572 loss_iou_aware: 0.863830 loss_obj: 5.574471 loss_cls: 0.225458 loss: 11.782482 eta: 0:26:20 batch_cost: 0.8730 data_cost: 0.1297 ips: 13.7458 images/s\n",
      "[08/13 16:07:26] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:07:26] ppdet.engine INFO: Epoch: [205] [ 0/36] learning_rate: 0.003330 loss_xy: 0.808422 loss_wh: 0.957183 loss_iou: 3.159632 loss_iou_aware: 0.803217 loss_obj: 5.396054 loss_cls: 0.234168 loss: 11.273219 eta: 0:26:10 batch_cost: 0.6908 data_cost: 0.0777 ips: 17.3721 images/s\n",
      "[08/13 16:07:44] ppdet.engine INFO: Epoch: [205] [20/36] learning_rate: 0.003330 loss_xy: 0.825003 loss_wh: 0.975002 loss_iou: 3.255965 loss_iou_aware: 0.757921 loss_obj: 5.354767 loss_cls: 0.154907 loss: 11.210749 eta: 0:25:57 batch_cost: 0.7542 data_cost: 0.0394 ips: 15.9099 images/s\n",
      "[08/13 16:07:58] ppdet.engine INFO: Epoch: [206] [ 0/36] learning_rate: 0.003330 loss_xy: 0.817014 loss_wh: 0.971589 loss_iou: 3.244318 loss_iou_aware: 0.785786 loss_obj: 5.439529 loss_cls: 0.162058 loss: 11.710320 eta: 0:25:47 batch_cost: 0.7164 data_cost: 0.0382 ips: 16.7507 images/s\n",
      "[08/13 16:08:12] ppdet.engine INFO: Epoch: [206] [20/36] learning_rate: 0.003330 loss_xy: 0.792696 loss_wh: 0.848596 loss_iou: 2.984851 loss_iou_aware: 0.755274 loss_obj: 5.565150 loss_cls: 0.180050 loss: 11.067198 eta: 0:25:33 batch_cost: 0.6108 data_cost: 0.0003 ips: 19.6471 images/s\n",
      "[08/13 16:08:26] ppdet.engine INFO: Epoch: [207] [ 0/36] learning_rate: 0.003330 loss_xy: 0.825673 loss_wh: 1.026385 loss_iou: 3.500660 loss_iou_aware: 0.845302 loss_obj: 5.111827 loss_cls: 0.170893 loss: 11.251966 eta: 0:25:22 batch_cost: 0.6530 data_cost: 0.0211 ips: 18.3772 images/s\n",
      "[08/13 16:08:42] ppdet.engine INFO: Epoch: [207] [20/36] learning_rate: 0.003330 loss_xy: 0.812146 loss_wh: 0.868591 loss_iou: 3.013948 loss_iou_aware: 0.743821 loss_obj: 5.058728 loss_cls: 0.189695 loss: 10.589232 eta: 0:25:09 batch_cost: 0.6640 data_cost: 0.0003 ips: 18.0711 images/s\n",
      "[08/13 16:08:59] ppdet.engine INFO: Epoch: [208] [ 0/36] learning_rate: 0.003330 loss_xy: 0.773497 loss_wh: 0.904040 loss_iou: 2.941591 loss_iou_aware: 0.740708 loss_obj: 5.093032 loss_cls: 0.160929 loss: 11.176950 eta: 0:24:58 batch_cost: 0.7811 data_cost: 0.0063 ips: 15.3624 images/s\n",
      "[08/13 16:09:15] ppdet.engine INFO: Epoch: [208] [20/36] learning_rate: 0.003330 loss_xy: 0.746497 loss_wh: 0.836482 loss_iou: 2.876287 loss_iou_aware: 0.738075 loss_obj: 4.715316 loss_cls: 0.186738 loss: 10.699499 eta: 0:24:45 batch_cost: 0.6544 data_cost: 0.0003 ips: 18.3368 images/s\n",
      "[08/13 16:09:30] ppdet.engine INFO: Epoch: [209] [ 0/36] learning_rate: 0.003330 loss_xy: 0.846964 loss_wh: 0.987761 loss_iou: 3.306249 loss_iou_aware: 0.805718 loss_obj: 5.290030 loss_cls: 0.171846 loss: 11.611469 eta: 0:24:34 batch_cost: 0.6925 data_cost: 0.0076 ips: 17.3293 images/s\n",
      "[08/13 16:09:45] ppdet.engine INFO: Epoch: [209] [20/36] learning_rate: 0.003330 loss_xy: 0.801179 loss_wh: 0.942962 loss_iou: 3.078322 loss_iou_aware: 0.780680 loss_obj: 4.915942 loss_cls: 0.172844 loss: 10.682281 eta: 0:24:21 batch_cost: 0.6495 data_cost: 0.0003 ips: 18.4755 images/s\n",
      "[08/13 16:09:59] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:10:00] ppdet.engine INFO: Epoch: [210] [ 0/36] learning_rate: 0.003330 loss_xy: 0.818404 loss_wh: 1.049990 loss_iou: 3.602970 loss_iou_aware: 0.863202 loss_obj: 5.081787 loss_cls: 0.191120 loss: 11.690583 eta: 0:24:10 batch_cost: 0.5857 data_cost: 0.0002 ips: 20.4899 images/s\n",
      "[08/13 16:10:16] ppdet.engine INFO: Epoch: [210] [20/36] learning_rate: 0.003330 loss_xy: 0.826472 loss_wh: 1.314804 loss_iou: 3.652912 loss_iou_aware: 0.881165 loss_obj: 5.157907 loss_cls: 0.213795 loss: 12.519178 eta: 0:23:56 batch_cost: 0.6683 data_cost: 0.0003 ips: 17.9568 images/s\n",
      "[08/13 16:10:28] ppdet.engine INFO: Epoch: [211] [ 0/36] learning_rate: 0.003330 loss_xy: 0.728898 loss_wh: 1.058593 loss_iou: 3.324921 loss_iou_aware: 0.790253 loss_obj: 4.814714 loss_cls: 0.242843 loss: 10.634623 eta: 0:23:45 batch_cost: 0.5939 data_cost: 0.0129 ips: 20.2070 images/s\n",
      "[08/13 16:10:44] ppdet.engine INFO: Epoch: [211] [20/36] learning_rate: 0.003330 loss_xy: 0.756711 loss_wh: 0.877558 loss_iou: 2.968871 loss_iou_aware: 0.748006 loss_obj: 4.812599 loss_cls: 0.189306 loss: 10.314909 eta: 0:23:31 batch_cost: 0.6378 data_cost: 0.0004 ips: 18.8161 images/s\n",
      "[08/13 16:10:58] ppdet.engine INFO: Epoch: [212] [ 0/36] learning_rate: 0.003330 loss_xy: 0.864330 loss_wh: 0.967959 loss_iou: 3.379282 loss_iou_aware: 0.819811 loss_obj: 5.555924 loss_cls: 0.161283 loss: 12.042196 eta: 0:23:21 batch_cost: 0.6818 data_cost: 0.0253 ips: 17.6014 images/s\n",
      "[08/13 16:11:15] ppdet.engine INFO: Epoch: [212] [20/36] learning_rate: 0.003330 loss_xy: 0.726296 loss_wh: 0.925070 loss_iou: 3.185054 loss_iou_aware: 0.793940 loss_obj: 5.034883 loss_cls: 0.170905 loss: 10.671159 eta: 0:23:08 batch_cost: 0.6986 data_cost: 0.0003 ips: 17.1781 images/s\n",
      "[08/13 16:11:29] ppdet.engine INFO: Epoch: [213] [ 0/36] learning_rate: 0.003330 loss_xy: 0.748606 loss_wh: 1.013139 loss_iou: 3.350146 loss_iou_aware: 0.797649 loss_obj: 5.077795 loss_cls: 0.133262 loss: 10.833462 eta: 0:22:57 batch_cost: 0.7377 data_cost: 0.0360 ips: 16.2675 images/s\n",
      "[08/13 16:11:45] ppdet.engine INFO: Epoch: [213] [20/36] learning_rate: 0.003330 loss_xy: 0.760764 loss_wh: 0.905182 loss_iou: 3.150988 loss_iou_aware: 0.785787 loss_obj: 5.193821 loss_cls: 0.165402 loss: 11.008020 eta: 0:22:44 batch_cost: 0.6601 data_cost: 0.0003 ips: 18.1801 images/s\n",
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      "[08/13 16:12:29] ppdet.engine INFO: Epoch: [215] [ 0/36] learning_rate: 0.003330 loss_xy: 0.760577 loss_wh: 1.005494 loss_iou: 3.202206 loss_iou_aware: 0.778770 loss_obj: 4.728855 loss_cls: 0.178659 loss: 10.916241 eta: 0:22:09 batch_cost: 0.5916 data_cost: 0.0010 ips: 20.2846 images/s\n",
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      "[08/13 16:15:03] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:15:04] ppdet.engine INFO: Epoch: [220] [ 0/36] learning_rate: 0.000333 loss_xy: 0.726744 loss_wh: 0.602396 loss_iou: 2.589626 loss_iou_aware: 0.707179 loss_obj: 4.491042 loss_cls: 0.100725 loss: 9.023233 eta: 0:20:08 batch_cost: 0.6906 data_cost: 0.0003 ips: 17.3767 images/s\n",
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      "[08/13 16:17:28] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:17:29] ppdet.engine INFO: Epoch: [225] [ 0/36] learning_rate: 0.000333 loss_xy: 0.709452 loss_wh: 0.623894 loss_iou: 2.398267 loss_iou_aware: 0.687837 loss_obj: 4.466076 loss_cls: 0.064371 loss: 9.192972 eta: 0:18:06 batch_cost: 0.6074 data_cost: 0.0003 ips: 19.7574 images/s\n",
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      "[08/13 16:18:31] ppdet.engine INFO: Epoch: [227] [ 0/36] learning_rate: 0.000333 loss_xy: 0.629559 loss_wh: 0.515280 loss_iou: 2.165771 loss_iou_aware: 0.621207 loss_obj: 4.014571 loss_cls: 0.059084 loss: 8.111807 eta: 0:17:19 batch_cost: 0.7676 data_cost: 0.0876 ips: 15.6328 images/s\n",
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      "[08/13 16:19:18] ppdet.engine INFO: Epoch: [228] [20/36] learning_rate: 0.000333 loss_xy: 0.764061 loss_wh: 0.636439 loss_iou: 2.594510 loss_iou_aware: 0.699618 loss_obj: 4.333239 loss_cls: 0.068159 loss: 8.829679 eta: 0:16:41 batch_cost: 0.6649 data_cost: 0.0003 ips: 18.0476 images/s\n",
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      "[08/13 16:20:04] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:20:04] ppdet.engine INFO: Epoch: [230] [ 0/36] learning_rate: 0.000333 loss_xy: 0.675721 loss_wh: 0.612298 loss_iou: 2.485050 loss_iou_aware: 0.658616 loss_obj: 4.098342 loss_cls: 0.058157 loss: 8.460610 eta: 0:16:06 batch_cost: 0.6194 data_cost: 0.0003 ips: 19.3744 images/s\n",
      "[08/13 16:20:20] ppdet.engine INFO: Epoch: [230] [20/36] learning_rate: 0.000333 loss_xy: 0.727895 loss_wh: 0.679178 loss_iou: 2.565079 loss_iou_aware: 0.670648 loss_obj: 4.471974 loss_cls: 0.060775 loss: 9.267504 eta: 0:15:53 batch_cost: 0.6630 data_cost: 0.0003 ips: 18.0983 images/s\n",
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      "[08/13 16:21:08] ppdet.engine INFO: Epoch: [232] [ 0/36] learning_rate: 0.000333 loss_xy: 0.806085 loss_wh: 0.617572 loss_iou: 2.565950 loss_iou_aware: 0.692393 loss_obj: 4.236950 loss_cls: 0.054628 loss: 9.188612 eta: 0:15:19 batch_cost: 0.7655 data_cost: 0.0402 ips: 15.6767 images/s\n",
      "[08/13 16:21:25] ppdet.engine INFO: Epoch: [232] [20/36] learning_rate: 0.000333 loss_xy: 0.674526 loss_wh: 0.550290 loss_iou: 2.222834 loss_iou_aware: 0.620360 loss_obj: 3.726461 loss_cls: 0.052534 loss: 7.911274 eta: 0:15:05 batch_cost: 0.7280 data_cost: 0.0003 ips: 16.4825 images/s\n",
      "[08/13 16:21:39] ppdet.engine INFO: Epoch: [233] [ 0/36] learning_rate: 0.000333 loss_xy: 0.678123 loss_wh: 0.577295 loss_iou: 2.483607 loss_iou_aware: 0.671364 loss_obj: 3.907125 loss_cls: 0.061592 loss: 8.175501 eta: 0:14:55 batch_cost: 0.6715 data_cost: 0.0548 ips: 17.8704 images/s\n",
      "[08/13 16:21:55] ppdet.engine INFO: Epoch: [233] [20/36] learning_rate: 0.000333 loss_xy: 0.761497 loss_wh: 0.569108 loss_iou: 2.326180 loss_iou_aware: 0.665144 loss_obj: 3.852179 loss_cls: 0.058902 loss: 8.074805 eta: 0:14:41 batch_cost: 0.6997 data_cost: 0.0003 ips: 17.1496 images/s\n",
      "[08/13 16:22:09] ppdet.engine INFO: Epoch: [234] [ 0/36] learning_rate: 0.000333 loss_xy: 0.708476 loss_wh: 0.620933 loss_iou: 2.407802 loss_iou_aware: 0.648874 loss_obj: 3.971723 loss_cls: 0.054174 loss: 8.541527 eta: 0:14:31 batch_cost: 0.6561 data_cost: 0.0569 ips: 18.2886 images/s\n",
      "[08/13 16:22:23] ppdet.engine INFO: Epoch: [234] [20/36] learning_rate: 0.000333 loss_xy: 0.701831 loss_wh: 0.582676 loss_iou: 2.369212 loss_iou_aware: 0.645687 loss_obj: 3.711180 loss_cls: 0.060944 loss: 8.074840 eta: 0:14:17 batch_cost: 0.5534 data_cost: 0.0002 ips: 21.6829 images/s\n",
      "[08/13 16:22:37] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:22:38] ppdet.engine INFO: Epoch: [235] [ 0/36] learning_rate: 0.000333 loss_xy: 0.750730 loss_wh: 0.608239 loss_iou: 2.588116 loss_iou_aware: 0.695643 loss_obj: 3.990929 loss_cls: 0.064563 loss: 8.619639 eta: 0:14:06 batch_cost: 0.6487 data_cost: 0.0002 ips: 18.4976 images/s\n",
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      "[08/13 16:23:09] ppdet.engine INFO: Epoch: [236] [ 0/36] learning_rate: 0.000333 loss_xy: 0.700071 loss_wh: 0.603236 loss_iou: 2.448132 loss_iou_aware: 0.669758 loss_obj: 3.851995 loss_cls: 0.046475 loss: 8.458282 eta: 0:13:42 batch_cost: 0.5975 data_cost: 0.0047 ips: 20.0836 images/s\n",
      "[08/13 16:23:26] ppdet.engine INFO: Epoch: [236] [20/36] learning_rate: 0.000333 loss_xy: 0.682699 loss_wh: 0.622706 loss_iou: 2.429551 loss_iou_aware: 0.673463 loss_obj: 4.015962 loss_cls: 0.045366 loss: 8.468586 eta: 0:13:29 batch_cost: 0.7478 data_cost: 0.0003 ips: 16.0478 images/s\n",
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      "[08/13 16:24:12] ppdet.engine INFO: Epoch: [238] [ 0/36] learning_rate: 0.000333 loss_xy: 0.646110 loss_wh: 0.611207 loss_iou: 2.405215 loss_iou_aware: 0.649237 loss_obj: 3.583223 loss_cls: 0.047724 loss: 8.155939 eta: 0:12:54 batch_cost: 0.7736 data_cost: 0.0610 ips: 15.5113 images/s\n",
      "[08/13 16:24:28] ppdet.engine INFO: Epoch: [238] [20/36] learning_rate: 0.000333 loss_xy: 0.682516 loss_wh: 0.543964 loss_iou: 2.333582 loss_iou_aware: 0.646893 loss_obj: 3.829085 loss_cls: 0.049234 loss: 7.995148 eta: 0:12:41 batch_cost: 0.7182 data_cost: 0.0003 ips: 16.7077 images/s\n",
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      "[08/13 16:25:14] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:25:15] ppdet.engine INFO: Epoch: [240] [ 0/36] learning_rate: 0.000333 loss_xy: 0.777436 loss_wh: 0.597752 loss_iou: 2.459262 loss_iou_aware: 0.669976 loss_obj: 4.019254 loss_cls: 0.051583 loss: 8.802720 eta: 0:12:06 batch_cost: 0.6502 data_cost: 0.0003 ips: 18.4561 images/s\n",
      "[08/13 16:25:29] ppdet.engine INFO: Epoch: [240] [20/36] learning_rate: 0.000333 loss_xy: 0.654316 loss_wh: 0.611056 loss_iou: 2.475240 loss_iou_aware: 0.670503 loss_obj: 4.093944 loss_cls: 0.047426 loss: 8.583925 eta: 0:11:52 batch_cost: 0.6042 data_cost: 0.0003 ips: 19.8620 images/s\n",
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      "[08/13 16:26:00] ppdet.engine INFO: Epoch: [241] [20/36] learning_rate: 0.000333 loss_xy: 0.611146 loss_wh: 0.560983 loss_iou: 2.262018 loss_iou_aware: 0.622006 loss_obj: 3.460670 loss_cls: 0.045618 loss: 7.549534 eta: 0:11:28 batch_cost: 0.6629 data_cost: 0.0003 ips: 18.1027 images/s\n",
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      "[08/13 16:26:42] ppdet.engine INFO: Epoch: [243] [ 0/36] learning_rate: 0.000033 loss_xy: 0.655482 loss_wh: 0.558254 loss_iou: 2.339691 loss_iou_aware: 0.630347 loss_obj: 3.688663 loss_cls: 0.042988 loss: 7.695726 eta: 0:10:53 batch_cost: 0.6786 data_cost: 0.0287 ips: 17.6832 images/s\n",
      "[08/13 16:26:58] ppdet.engine INFO: Epoch: [243] [20/36] learning_rate: 0.000033 loss_xy: 0.685326 loss_wh: 0.595905 loss_iou: 2.519339 loss_iou_aware: 0.685057 loss_obj: 4.151680 loss_cls: 0.041871 loss: 8.463979 eta: 0:10:40 batch_cost: 0.6629 data_cost: 0.0003 ips: 18.1017 images/s\n",
      "[08/13 16:27:14] ppdet.engine INFO: Epoch: [244] [ 0/36] learning_rate: 0.000033 loss_xy: 0.645413 loss_wh: 0.567892 loss_iou: 2.387420 loss_iou_aware: 0.639830 loss_obj: 3.651680 loss_cls: 0.051350 loss: 8.159420 eta: 0:10:29 batch_cost: 0.7370 data_cost: 0.0451 ips: 16.2825 images/s\n",
      "[08/13 16:27:31] ppdet.engine INFO: Epoch: [244] [20/36] learning_rate: 0.000033 loss_xy: 0.711269 loss_wh: 0.518032 loss_iou: 2.316045 loss_iou_aware: 0.639909 loss_obj: 4.068739 loss_cls: 0.039860 loss: 8.173684 eta: 0:10:16 batch_cost: 0.6959 data_cost: 0.0003 ips: 17.2450 images/s\n",
      "[08/13 16:27:45] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:27:46] ppdet.engine INFO: Epoch: [245] [ 0/36] learning_rate: 0.000033 loss_xy: 0.675339 loss_wh: 0.553079 loss_iou: 2.336554 loss_iou_aware: 0.637955 loss_obj: 3.543392 loss_cls: 0.047255 loss: 7.383232 eta: 0:10:05 batch_cost: 0.6247 data_cost: 0.0003 ips: 19.2103 images/s\n",
      "[08/13 16:28:01] ppdet.engine INFO: Epoch: [245] [20/36] learning_rate: 0.000033 loss_xy: 0.670076 loss_wh: 0.532437 loss_iou: 2.247178 loss_iou_aware: 0.622761 loss_obj: 3.759680 loss_cls: 0.049002 loss: 7.745922 eta: 0:09:51 batch_cost: 0.6591 data_cost: 0.0003 ips: 18.2058 images/s\n",
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      "[08/13 16:28:45] ppdet.engine INFO: Epoch: [247] [ 0/36] learning_rate: 0.000033 loss_xy: 0.641358 loss_wh: 0.540129 loss_iou: 2.187717 loss_iou_aware: 0.623397 loss_obj: 3.886006 loss_cls: 0.047182 loss: 8.157427 eta: 0:09:16 batch_cost: 0.6830 data_cost: 0.0154 ips: 17.5706 images/s\n",
      "[08/13 16:29:01] ppdet.engine INFO: Epoch: [247] [20/36] learning_rate: 0.000033 loss_xy: 0.675555 loss_wh: 0.623240 loss_iou: 2.461091 loss_iou_aware: 0.679606 loss_obj: 4.152858 loss_cls: 0.045881 loss: 8.554764 eta: 0:09:03 batch_cost: 0.7209 data_cost: 0.0003 ips: 16.6467 images/s\n",
      "[08/13 16:29:16] ppdet.engine INFO: Epoch: [248] [ 0/36] learning_rate: 0.000033 loss_xy: 0.654991 loss_wh: 0.584687 loss_iou: 2.350044 loss_iou_aware: 0.637630 loss_obj: 3.735953 loss_cls: 0.043052 loss: 8.065629 eta: 0:08:52 batch_cost: 0.6786 data_cost: 0.0675 ips: 17.6845 images/s\n",
      "[08/13 16:29:30] ppdet.engine INFO: Epoch: [248] [20/36] learning_rate: 0.000033 loss_xy: 0.627650 loss_wh: 0.578989 loss_iou: 2.468353 loss_iou_aware: 0.701903 loss_obj: 3.707974 loss_cls: 0.046849 loss: 8.119719 eta: 0:08:39 batch_cost: 0.6121 data_cost: 0.0003 ips: 19.6044 images/s\n",
      "[08/13 16:29:44] ppdet.engine INFO: Epoch: [249] [ 0/36] learning_rate: 0.000033 loss_xy: 0.649359 loss_wh: 0.520949 loss_iou: 2.174596 loss_iou_aware: 0.607844 loss_obj: 3.608515 loss_cls: 0.045358 loss: 7.687665 eta: 0:08:28 batch_cost: 0.7029 data_cost: 0.0665 ips: 17.0724 images/s\n",
      "[08/13 16:30:01] ppdet.engine INFO: Epoch: [249] [20/36] learning_rate: 0.000033 loss_xy: 0.662761 loss_wh: 0.564831 loss_iou: 2.503484 loss_iou_aware: 0.692827 loss_obj: 3.902860 loss_cls: 0.043505 loss: 8.636324 eta: 0:08:14 batch_cost: 0.6947 data_cost: 0.0003 ips: 17.2745 images/s\n",
      "[08/13 16:30:15] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:30:16] ppdet.engine INFO: Epoch: [250] [ 0/36] learning_rate: 0.000033 loss_xy: 0.713115 loss_wh: 0.556928 loss_iou: 2.268691 loss_iou_aware: 0.627810 loss_obj: 3.574626 loss_cls: 0.041819 loss: 8.012211 eta: 0:08:04 batch_cost: 0.6552 data_cost: 0.0003 ips: 18.3141 images/s\n",
      "[08/13 16:30:32] ppdet.engine INFO: Epoch: [250] [20/36] learning_rate: 0.000033 loss_xy: 0.693045 loss_wh: 0.541493 loss_iou: 2.260806 loss_iou_aware: 0.672598 loss_obj: 3.812037 loss_cls: 0.042882 loss: 8.311590 eta: 0:07:50 batch_cost: 0.6775 data_cost: 0.0003 ips: 17.7111 images/s\n",
      "[08/13 16:30:46] ppdet.engine INFO: Epoch: [251] [ 0/36] learning_rate: 0.000033 loss_xy: 0.682721 loss_wh: 0.542750 loss_iou: 2.361163 loss_iou_aware: 0.661821 loss_obj: 4.010425 loss_cls: 0.044338 loss: 8.348480 eta: 0:07:39 batch_cost: 0.6147 data_cost: 0.0003 ips: 19.5226 images/s\n",
      "[08/13 16:31:00] ppdet.engine INFO: Epoch: [251] [20/36] learning_rate: 0.000033 loss_xy: 0.662919 loss_wh: 0.515931 loss_iou: 2.258912 loss_iou_aware: 0.641850 loss_obj: 3.463449 loss_cls: 0.043365 loss: 7.717853 eta: 0:07:26 batch_cost: 0.5667 data_cost: 0.0003 ips: 21.1737 images/s\n",
      "[08/13 16:31:14] ppdet.engine INFO: Epoch: [252] [ 0/36] learning_rate: 0.000033 loss_xy: 0.705560 loss_wh: 0.620536 loss_iou: 2.508144 loss_iou_aware: 0.678327 loss_obj: 3.740053 loss_cls: 0.048261 loss: 8.427050 eta: 0:07:15 batch_cost: 0.6385 data_cost: 0.0625 ips: 18.7945 images/s\n",
      "[08/13 16:31:31] ppdet.engine INFO: Epoch: [252] [20/36] learning_rate: 0.000033 loss_xy: 0.685474 loss_wh: 0.602098 loss_iou: 2.374222 loss_iou_aware: 0.668753 loss_obj: 3.732183 loss_cls: 0.053589 loss: 8.211782 eta: 0:07:02 batch_cost: 0.7216 data_cost: 0.0003 ips: 16.6298 images/s\n",
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      "[08/13 16:31:57] ppdet.engine INFO: Epoch: [253] [20/36] learning_rate: 0.000033 loss_xy: 0.658106 loss_wh: 0.513892 loss_iou: 2.394584 loss_iou_aware: 0.651101 loss_obj: 3.470474 loss_cls: 0.042549 loss: 7.885749 eta: 0:06:37 batch_cost: 0.5519 data_cost: 0.0003 ips: 21.7417 images/s\n",
      "[08/13 16:32:09] ppdet.engine INFO: Epoch: [254] [ 0/36] learning_rate: 0.000033 loss_xy: 0.686533 loss_wh: 0.553418 loss_iou: 2.401782 loss_iou_aware: 0.639027 loss_obj: 3.565519 loss_cls: 0.047366 loss: 7.817485 eta: 0:06:26 batch_cost: 0.5478 data_cost: 0.0148 ips: 21.9072 images/s\n",
      "[08/13 16:32:27] ppdet.engine INFO: Epoch: [254] [20/36] learning_rate: 0.000033 loss_xy: 0.649772 loss_wh: 0.562236 loss_iou: 2.204598 loss_iou_aware: 0.621623 loss_obj: 3.638493 loss_cls: 0.037718 loss: 7.612507 eta: 0:06:13 batch_cost: 0.7931 data_cost: 0.0003 ips: 15.1312 images/s\n",
      "[08/13 16:32:40] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:32:41] ppdet.engine INFO: Epoch: [255] [ 0/36] learning_rate: 0.000033 loss_xy: 0.664417 loss_wh: 0.542031 loss_iou: 2.131213 loss_iou_aware: 0.596464 loss_obj: 3.567136 loss_cls: 0.041855 loss: 7.545575 eta: 0:06:02 batch_cost: 0.5660 data_cost: 0.0003 ips: 21.2030 images/s\n",
      "[08/13 16:32:57] ppdet.engine INFO: Epoch: [255] [20/36] learning_rate: 0.000033 loss_xy: 0.642733 loss_wh: 0.548645 loss_iou: 2.315259 loss_iou_aware: 0.664169 loss_obj: 3.916673 loss_cls: 0.052424 loss: 8.321591 eta: 0:05:49 batch_cost: 0.6369 data_cost: 0.0003 ips: 18.8407 images/s\n",
      "[08/13 16:33:11] ppdet.engine INFO: Epoch: [256] [ 0/36] learning_rate: 0.000033 loss_xy: 0.691751 loss_wh: 0.600302 loss_iou: 2.521683 loss_iou_aware: 0.729204 loss_obj: 3.944030 loss_cls: 0.047651 loss: 8.713449 eta: 0:05:38 batch_cost: 0.6217 data_cost: 0.0272 ips: 19.3011 images/s\n",
      "[08/13 16:33:26] ppdet.engine INFO: Epoch: [256] [20/36] learning_rate: 0.000033 loss_xy: 0.675330 loss_wh: 0.540165 loss_iou: 2.246464 loss_iou_aware: 0.645507 loss_obj: 3.882129 loss_cls: 0.043269 loss: 8.448540 eta: 0:05:25 batch_cost: 0.6259 data_cost: 0.0003 ips: 19.1736 images/s\n",
      "[08/13 16:33:41] ppdet.engine INFO: Epoch: [257] [ 0/36] learning_rate: 0.000033 loss_xy: 0.658007 loss_wh: 0.548068 loss_iou: 2.298890 loss_iou_aware: 0.649057 loss_obj: 3.755680 loss_cls: 0.042588 loss: 8.313962 eta: 0:05:14 batch_cost: 0.7030 data_cost: 0.0352 ips: 17.0693 images/s\n",
      "[08/13 16:33:56] ppdet.engine INFO: Epoch: [257] [20/36] learning_rate: 0.000033 loss_xy: 0.718679 loss_wh: 0.552849 loss_iou: 2.326531 loss_iou_aware: 0.643549 loss_obj: 3.617485 loss_cls: 0.048591 loss: 7.998276 eta: 0:05:00 batch_cost: 0.6511 data_cost: 0.0003 ips: 18.4305 images/s\n",
      "[08/13 16:34:11] ppdet.engine INFO: Epoch: [258] [ 0/36] learning_rate: 0.000033 loss_xy: 0.689484 loss_wh: 0.530455 loss_iou: 2.379416 loss_iou_aware: 0.651826 loss_obj: 3.637053 loss_cls: 0.041528 loss: 7.894141 eta: 0:04:50 batch_cost: 0.7203 data_cost: 0.0737 ips: 16.6608 images/s\n",
      "[08/13 16:34:27] ppdet.engine INFO: Epoch: [258] [20/36] learning_rate: 0.000033 loss_xy: 0.656280 loss_wh: 0.458501 loss_iou: 1.965851 loss_iou_aware: 0.573082 loss_obj: 3.263479 loss_cls: 0.037976 loss: 6.934764 eta: 0:04:36 batch_cost: 0.7093 data_cost: 0.0553 ips: 16.9175 images/s\n",
      "[08/13 16:34:41] ppdet.engine INFO: Epoch: [259] [ 0/36] learning_rate: 0.000033 loss_xy: 0.672298 loss_wh: 0.492648 loss_iou: 2.201134 loss_iou_aware: 0.625337 loss_obj: 3.867673 loss_cls: 0.040808 loss: 7.889393 eta: 0:04:26 batch_cost: 0.6550 data_cost: 0.0167 ips: 18.3199 images/s\n",
      "[08/13 16:34:54] ppdet.engine INFO: Epoch: [259] [20/36] learning_rate: 0.000033 loss_xy: 0.698749 loss_wh: 0.574575 loss_iou: 2.685509 loss_iou_aware: 0.698606 loss_obj: 3.941994 loss_cls: 0.044002 loss: 8.854356 eta: 0:04:12 batch_cost: 0.5765 data_cost: 0.0003 ips: 20.8147 images/s\n",
      "[08/13 16:35:08] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:35:09] ppdet.engine INFO: Epoch: [260] [ 0/36] learning_rate: 0.000033 loss_xy: 0.634955 loss_wh: 0.451883 loss_iou: 2.045030 loss_iou_aware: 0.576775 loss_obj: 3.484425 loss_cls: 0.042868 loss: 7.322092 eta: 0:04:01 batch_cost: 0.6012 data_cost: 0.0003 ips: 19.9602 images/s\n",
      "[08/13 16:35:23] ppdet.engine INFO: Epoch: [260] [20/36] learning_rate: 0.000033 loss_xy: 0.667564 loss_wh: 0.565622 loss_iou: 2.386598 loss_iou_aware: 0.676140 loss_obj: 3.914003 loss_cls: 0.046887 loss: 8.343317 eta: 0:03:48 batch_cost: 0.6011 data_cost: 0.0003 ips: 19.9645 images/s\n",
      "[08/13 16:35:40] ppdet.engine INFO: Epoch: [261] [ 0/36] learning_rate: 0.000033 loss_xy: 0.641175 loss_wh: 0.532407 loss_iou: 2.175042 loss_iou_aware: 0.607657 loss_obj: 3.878298 loss_cls: 0.039169 loss: 7.752088 eta: 0:03:37 batch_cost: 0.7771 data_cost: 0.0910 ips: 15.4428 images/s\n",
      "[08/13 16:35:58] ppdet.engine INFO: Epoch: [261] [20/36] learning_rate: 0.000033 loss_xy: 0.697850 loss_wh: 0.550889 loss_iou: 2.432134 loss_iou_aware: 0.668461 loss_obj: 3.721344 loss_cls: 0.043974 loss: 8.194584 eta: 0:03:24 batch_cost: 0.7814 data_cost: 0.0003 ips: 15.3573 images/s\n",
      "[08/13 16:36:12] ppdet.engine INFO: Epoch: [262] [ 0/36] learning_rate: 0.000033 loss_xy: 0.660571 loss_wh: 0.538693 loss_iou: 2.366237 loss_iou_aware: 0.665675 loss_obj: 3.936530 loss_cls: 0.045453 loss: 7.912688 eta: 0:03:13 batch_cost: 0.6581 data_cost: 0.0374 ips: 18.2341 images/s\n",
      "[08/13 16:36:30] ppdet.engine INFO: Epoch: [262] [20/36] learning_rate: 0.000033 loss_xy: 0.705245 loss_wh: 0.518061 loss_iou: 2.241920 loss_iou_aware: 0.629489 loss_obj: 3.677228 loss_cls: 0.041112 loss: 7.728279 eta: 0:03:00 batch_cost: 0.7446 data_cost: 0.0174 ips: 16.1169 images/s\n",
      "[08/13 16:36:47] ppdet.engine INFO: Epoch: [263] [ 0/36] learning_rate: 0.000033 loss_xy: 0.735092 loss_wh: 0.520240 loss_iou: 2.340022 loss_iou_aware: 0.669394 loss_obj: 3.719630 loss_cls: 0.041575 loss: 8.320972 eta: 0:02:49 batch_cost: 0.8779 data_cost: 0.0864 ips: 13.6693 images/s\n",
      "[08/13 16:37:03] ppdet.engine INFO: Epoch: [263] [20/36] learning_rate: 0.000033 loss_xy: 0.654124 loss_wh: 0.595259 loss_iou: 2.426123 loss_iou_aware: 0.663141 loss_obj: 3.518241 loss_cls: 0.038365 loss: 7.795209 eta: 0:02:35 batch_cost: 0.6913 data_cost: 0.0003 ips: 17.3588 images/s\n",
      "[08/13 16:37:19] ppdet.engine INFO: Epoch: [264] [ 0/36] learning_rate: 0.000033 loss_xy: 0.731697 loss_wh: 0.586147 loss_iou: 2.397974 loss_iou_aware: 0.666095 loss_obj: 3.922208 loss_cls: 0.041748 loss: 8.081121 eta: 0:02:25 batch_cost: 0.7704 data_cost: 0.0246 ips: 15.5771 images/s\n",
      "[08/13 16:37:35] ppdet.engine INFO: Epoch: [264] [20/36] learning_rate: 0.000033 loss_xy: 0.660956 loss_wh: 0.492185 loss_iou: 2.035121 loss_iou_aware: 0.599580 loss_obj: 3.574617 loss_cls: 0.040848 loss: 7.577375 eta: 0:02:11 batch_cost: 0.7181 data_cost: 0.0003 ips: 16.7106 images/s\n",
      "[08/13 16:37:50] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "[08/13 16:37:51] ppdet.engine INFO: Epoch: [265] [ 0/36] learning_rate: 0.000033 loss_xy: 0.687061 loss_wh: 0.577220 loss_iou: 2.378970 loss_iou_aware: 0.635122 loss_obj: 4.013115 loss_cls: 0.040142 loss: 8.542101 eta: 0:02:01 batch_cost: 0.6751 data_cost: 0.0003 ips: 17.7755 images/s\n",
      "[08/13 16:38:08] ppdet.engine INFO: Epoch: [265] [20/36] learning_rate: 0.000033 loss_xy: 0.658858 loss_wh: 0.542382 loss_iou: 2.287975 loss_iou_aware: 0.642303 loss_obj: 3.865932 loss_cls: 0.045466 loss: 8.174333 eta: 0:01:47 batch_cost: 0.7489 data_cost: 0.0003 ips: 16.0237 images/s\n",
      "[08/13 16:38:24] ppdet.engine INFO: Epoch: [266] [ 0/36] learning_rate: 0.000033 loss_xy: 0.701878 loss_wh: 0.574224 loss_iou: 2.405987 loss_iou_aware: 0.688801 loss_obj: 3.951996 loss_cls: 0.046217 loss: 8.962391 eta: 0:01:36 batch_cost: 0.6960 data_cost: 0.0007 ips: 17.2405 images/s\n",
      "[08/13 16:38:42] ppdet.engine INFO: Epoch: [266] [20/36] learning_rate: 0.000033 loss_xy: 0.655815 loss_wh: 0.530050 loss_iou: 2.096654 loss_iou_aware: 0.592571 loss_obj: 3.717846 loss_cls: 0.033586 loss: 7.531343 eta: 0:01:23 batch_cost: 0.7342 data_cost: 0.0003 ips: 16.3433 images/s\n",
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      "[08/13 16:39:13] ppdet.engine INFO: Epoch: [267] [20/36] learning_rate: 0.000033 loss_xy: 0.728594 loss_wh: 0.632244 loss_iou: 2.670212 loss_iou_aware: 0.719316 loss_obj: 3.744350 loss_cls: 0.038247 loss: 8.659031 eta: 0:00:59 batch_cost: 0.7533 data_cost: 0.0003 ips: 15.9307 images/s\n",
      "[08/13 16:39:27] ppdet.engine INFO: Epoch: [268] [ 0/36] learning_rate: 0.000033 loss_xy: 0.647032 loss_wh: 0.493551 loss_iou: 2.086792 loss_iou_aware: 0.613496 loss_obj: 3.488399 loss_cls: 0.039476 loss: 7.461312 eta: 0:00:48 batch_cost: 0.6967 data_cost: 0.0436 ips: 17.2238 images/s\n",
      "[08/13 16:39:43] ppdet.engine INFO: Epoch: [268] [20/36] learning_rate: 0.000033 loss_xy: 0.622548 loss_wh: 0.556880 loss_iou: 2.284518 loss_iou_aware: 0.638665 loss_obj: 3.547278 loss_cls: 0.037907 loss: 7.850287 eta: 0:00:35 batch_cost: 0.6834 data_cost: 0.0003 ips: 17.5596 images/s\n",
      "[08/13 16:39:58] ppdet.engine INFO: Epoch: [269] [ 0/36] learning_rate: 0.000033 loss_xy: 0.709433 loss_wh: 0.541249 loss_iou: 2.259555 loss_iou_aware: 0.647264 loss_obj: 3.948052 loss_cls: 0.042366 loss: 7.972075 eta: 0:00:24 batch_cost: 0.7034 data_cost: 0.0717 ips: 17.0589 images/s\n",
      "[08/13 16:40:13] ppdet.engine INFO: Epoch: [269] [20/36] learning_rate: 0.000033 loss_xy: 0.660084 loss_wh: 0.525862 loss_iou: 2.215893 loss_iou_aware: 0.606953 loss_obj: 3.458484 loss_cls: 0.036870 loss: 7.447184 eta: 0:00:10 batch_cost: 0.6397 data_cost: 0.0003 ips: 18.7602 images/s\n",
      "[08/13 16:40:28] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyolov2_r50vd_dcn_voc\n",
      "mv: cannot stat 'output/ppyolov2_r50vd_dcn_voc/best_model.pdparams': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "#启动训练\r\n",
    "# !python tools/train.py -c configs/yolov3/yolov3_darknet53_270e_voc.yml --eval --use_vdl=True --vdl_log_dir=\"./output\"\r\n",
    "# ! mv output/yolov3_darknet53_270e_voc/best_model.pdparams ../work/result_model/yolov3_darknet53_270e_voc\r\n",
    "\r\n",
    "!python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_voc.yml #--eval --use_vdl=True --vdl_log_dir=\"./output\"\r\n",
    "! mv output/ppyolov2_r50vd_dcn_voc/best_model.pdparams ../work/result_model/ppyolov2_r50vd_dcn_voc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**实验结果：**\n",
    "使用yolov3：\n",
    "参数列表：\n",
    "| epoch | lr | milestones | Map |\n",
    "| -------- | -------- | -------- | -------- |\n",
    "| 270     | 0.001     | 216，243     | 76.7     |\n",
    "| 100     | 0.001     | 50，80     | 78.0     |\n",
    "| 100     | 0.01     | 50，80     | 21.0     |\n",
    "| 100     | 0.005     | 50，80     | 29.6     |\n",
    "| 100     | 0.002     | 50，80     | 73.6     |\n",
    "\n",
    "\n",
    "使用ppyolov2：\n",
    "参数列表：\n",
    "| epoch | lr | milestones | Map |\n",
    "| -------- | -------- | -------- | -------- |\n",
    "| 270     | 0.00333     | 216，243     | 79.5     |\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 下述命令会在127.0.0.1上启动一个服务，支持通过前端web页面查看，可以通过--host这个参数指定实际ip地址\r\n",
    "visualdl --logdir output/\r\n",
    "# 或者直接也能够可视化工具将log文件打开，然后启动VisualDL就行了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if data.dtype == np.object:\n",
      "W0813 16:44:39.818972 27907 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0813 16:44:39.823820 27907 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[08/13 16:44:44] ppdet.utils.checkpoint INFO: Finish loading model weights: output/ppyolov2_r50vd_dcn_voc/259.pdparams\n",
      "[08/13 16:44:44] ppdet.engine INFO: Eval iter: 0\n",
      "[08/13 16:44:45] ppdet.metrics.metrics INFO: Accumulating evaluatation results...\n",
      "[08/13 16:44:45] ppdet.metrics.metrics INFO: mAP(0.50, 11point) = 79.19%\n",
      "[08/13 16:44:45] ppdet.engine INFO: Total sample number: 49, averge FPS: 30.30533405174003\n"
     ]
    }
   ],
   "source": [
    "#模型评估\r\n",
    "# %cd PaddleDetection/\r\n",
    "# !python -u tools/eval.py -c configs/yolov3/yolov3_darknet53_270e_voc.yml  -o weights=output/yolov3_darknet53_270e_voc/best_model.pdparams\r\n",
    "!python -u tools/eval.py -c configs/ppyolo/ppyolov2_r50vd_dcn_voc.yml  -o weights=output/ppyolov2_r50vd_dcn_voc/259.pdparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 模型推理\r\n",
    "# %cd PaddleDetection/\r\n",
    "!python tools/infer.py -c configs/yolov3/yolov3_darknet53_270e_voc.yml -o weights=output/yolov3_darknet53_270e_voc/best_model.pdparams --infer_img=dataset/fire/JPEGImages/113.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 导出配置文件\r\n",
    "!python -u tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_voc.yml --output_dir=./inference_model -o weights=output/yolov3_darknet53_270e_voc/best_model.pdparams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**使用PaddleX训练并使用PaddleHub部署**\n",
    "\n",
    "1. 使用PaddleX训练模型，并导出\n",
    "\n",
    "2. 将模型转换成PaddleHub的预训练模型\n",
    "\n",
    "3. 模型安装\n",
    "\n",
    "4. 模型部署\n",
    "\n",
    "5. 预测结果和效果展示\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio\n"
     ]
    }
   ],
   "source": [
    "# 将数据集fire.zip解压到MyDataset\r\n",
    "%cd ../\r\n",
    "!unzip -oq data/data104117/fire.zip -d MyDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/\n",
      "Collecting paddlex\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/d6/a2/07435f4aa1e51fe22bdf06c95d03bf1b78b7bc6625adbb51e35dc0804cc7/paddlex-1.3.11-py3-none-any.whl (516kB)\n",
      "\u001b[K     |████████████████████████████████| 522kB 15.8MB/s eta 0:00:01\n",
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      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/44/48/def306413b25c3d01753603b1a222a011b8621aed27cd7f89cbc27e6b0f4/xlwt-1.3.0-py2.py3-none-any.whl (99kB)\n",
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      "\u001b[?25hCollecting paddlehub==2.1.0 (from paddlex)\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/7a/29/3bd0ca43c787181e9c22fe44b944b64d7fcb14ce66d3bf4602d9ad2ac76c/paddlehub-2.1.0-py3-none-any.whl (211kB)\n",
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      "Collecting paddleslim==1.1.1 (from paddlex)\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/d1/77/e257227bed9a70ff0d35a4a3c4e70ac2d2362c803834c4c52018f7c4b762/paddleslim-1.1.1-py2.py3-none-any.whl (145kB)\n",
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      "\u001b[?25hRequirement already satisfied: psutil in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlex) (5.7.2)\n",
      "Collecting paddle2onnx>=0.5.1 (from paddlehub==2.1.0->paddlex)\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/37/80/aa6134b5f36aea45dc1b363e7af941dccabe4d7e167ac391ff046f34baf1/paddle2onnx-0.7-py3-none-any.whl (94kB)\n",
      "\u001b[K     |████████████████████████████████| 102kB 31.8MB/s ta 0:00:01\n",
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      "Requirement already satisfied: pycryptodome>=3.8.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl>=2.0.0->paddlex) (3.9.9)\n",
      "Requirement already satisfied: future>=0.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl>=2.0.0->paddlex) (0.18.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->sklearn->paddlex) (2.1.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->sklearn->paddlex) (1.6.3)\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn->sklearn->paddlex) (0.14.1)\n",
      "Requirement already satisfied: smmap<4,>=3.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from gitdb<5,>=4.0.1->gitpython->paddlehub==2.1.0->paddlex) (3.0.5)\n",
      "Requirement already satisfied: dill>=0.3.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from multiprocess->paddlenlp>=2.0.0rc5->paddlehub==2.1.0->paddlex) (0.3.3)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.10.1->flask>=1.1.0->paddlehub==2.1.0->paddlex) (1.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->flake8>=3.7.9->visualdl>=2.0.0->paddlex) (0.6.0)\n",
      "Requirement already satisfied: more-itertools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from zipp>=0.5->importlib-metadata; python_version < \"3.8\"->flake8>=3.7.9->visualdl>=2.0.0->paddlex) (7.2.0)\n",
      "Installing collected packages: xlwt, paddle2onnx, paddlehub, paddleslim, paddlex\n",
      "  Found existing installation: paddlehub 2.0.4\n",
      "    Uninstalling paddlehub-2.0.4:\n",
      "      Successfully uninstalled paddlehub-2.0.4\n",
      "Successfully installed paddle2onnx-0.7 paddlehub-2.1.0 paddleslim-1.1.1 paddlex-1.3.11 xlwt-1.3.0\n"
     ]
    }
   ],
   "source": [
    "# pip安装PaddleX\r\n",
    "!pip install paddlex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Split Done.\u001b[0m\r\n",
      "\u001b[0mTrain samples: 345\u001b[0m\r\n",
      "\u001b[0mEval samples: 98\u001b[0m\r\n",
      "\u001b[0mTest samples: 49\u001b[0m\r\n",
      "\u001b[0mSplit files saved in MyDataset/fire\u001b[0m\r\n",
      "\u001b[0m\u001b[0m"
     ]
    }
   ],
   "source": [
    "# 划分数据集\r\n",
    "!paddlex --split_dataset --format VOC --dataset_dir MyDataset/fire --val_value 0.2 --test_value 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 16:57:35 [INFO]\tStarting to read file list from dataset...\n",
      "2021-08-13 16:57:35 [INFO]\t345 samples in file MyDataset/fire/train_list.txt\n",
      "creating index...\n",
      "index created!\n",
      "2021-08-13 16:57:35 [INFO]\tStarting to read file list from dataset...\n",
      "2021-08-13 16:57:35 [INFO]\t98 samples in file MyDataset/fire/val_list.txt\n",
      "creating index...\n",
      "index created!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/framework.py:706: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif dtype == np.bool:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 16:57:37 [INFO]\tDecompressing output/yolov3_darknet53/pretrain/DarkNet53_ImageNet1k_pretrained.tar...\n",
      "2021-08-13 16:57:43 [INFO]\tLoad pretrain weights from output/yolov3_darknet53/pretrain/DarkNet53_ImageNet1k_pretrained.\n",
      "2021-08-13 16:57:44 [INFO]\tThere are 260 varaibles in output/yolov3_darknet53/pretrain/DarkNet53_ImageNet1k_pretrained are loaded.\n",
      "2021-08-13 16:57:50 [INFO]\t[TRAIN] Epoch=1/100, Step=2/43, loss=16932.625, lr=0.0, time_each_step=3.04s, eta=3:44:10\n",
      "2021-08-13 16:57:51 [INFO]\t[TRAIN] Epoch=1/100, Step=4/43, loss=10928.869141, lr=0.0, time_each_step=1.69s, eta=2:4:56\n",
      "2021-08-13 16:57:51 [INFO]\t[TRAIN] Epoch=1/100, Step=6/43, loss=4800.667969, lr=1e-06, time_each_step=1.2s, eta=1:28:49\n",
      "2021-08-13 16:57:52 [INFO]\t[TRAIN] Epoch=1/100, Step=8/43, loss=7650.993652, lr=1e-06, time_each_step=0.96s, eta=1:10:51\n",
      "2021-08-13 16:57:52 [INFO]\t[TRAIN] Epoch=1/100, Step=10/43, loss=6627.772949, lr=1e-06, time_each_step=0.85s, eta=1:2:22\n",
      "2021-08-13 16:57:53 [INFO]\t[TRAIN] Epoch=1/100, Step=12/43, loss=2327.6604, lr=1e-06, time_each_step=0.76s, eta=0:55:39\n",
      "2021-08-13 16:57:54 [INFO]\t[TRAIN] Epoch=1/100, Step=14/43, loss=2511.436035, lr=2e-06, time_each_step=0.7s, eta=0:51:20\n",
      "2021-08-13 16:57:54 [INFO]\t[TRAIN] Epoch=1/100, Step=16/43, loss=1890.428955, lr=2e-06, time_each_step=0.65s, eta=0:47:45\n",
      "2021-08-13 16:57:55 [INFO]\t[TRAIN] Epoch=1/100, Step=18/43, loss=1142.3479, lr=2e-06, time_each_step=0.61s, eta=0:44:45\n",
      "2021-08-13 16:57:55 [INFO]\t[TRAIN] Epoch=1/100, Step=20/43, loss=258.021545, lr=2e-06, time_each_step=0.57s, eta=0:41:35\n",
      "2021-08-13 16:57:56 [INFO]\t[TRAIN] Epoch=1/100, Step=22/43, loss=344.269684, lr=3e-06, time_each_step=0.29s, eta=0:21:16\n",
      "2021-08-13 16:57:56 [INFO]\t[TRAIN] Epoch=1/100, Step=24/43, loss=275.443909, lr=3e-06, time_each_step=0.28s, eta=0:20:27\n",
      "2021-08-13 16:57:57 [INFO]\t[TRAIN] Epoch=1/100, Step=26/43, loss=117.779785, lr=3e-06, time_each_step=0.28s, eta=0:20:39\n",
      "2021-08-13 16:57:57 [INFO]\t[TRAIN] Epoch=1/100, Step=28/43, loss=120.780968, lr=3e-06, time_each_step=0.28s, eta=0:20:42\n",
      "2021-08-13 16:57:58 [INFO]\t[TRAIN] Epoch=1/100, Step=30/43, loss=46.956799, lr=4e-06, time_each_step=0.26s, eta=0:19:5\n",
      "2021-08-13 16:57:58 [INFO]\t[TRAIN] Epoch=1/100, Step=32/43, loss=71.367882, lr=4e-06, time_each_step=0.25s, eta=0:18:3\n",
      "2021-08-13 16:57:58 [INFO]\t[TRAIN] Epoch=1/100, Step=34/43, loss=56.991207, lr=4e-06, time_each_step=0.23s, eta=0:16:56\n",
      "2021-08-13 16:57:59 [INFO]\t[TRAIN] Epoch=1/100, Step=36/43, loss=36.82119, lr=4e-06, time_each_step=0.22s, eta=0:15:55\n",
      "2021-08-13 16:57:59 [INFO]\t[TRAIN] Epoch=1/100, Step=38/43, loss=31.412773, lr=5e-06, time_each_step=0.2s, eta=0:15:0\n",
      "2021-08-13 16:57:59 [INFO]\t[TRAIN] Epoch=1/100, Step=40/43, loss=31.81394, lr=5e-06, time_each_step=0.2s, eta=0:14:49\n",
      "2021-08-13 16:58:00 [INFO]\t[TRAIN] Epoch=1/100, Step=42/43, loss=30.789232, lr=5e-06, time_each_step=0.2s, eta=0:14:20\n",
      "2021-08-13 16:58:00 [INFO]\t[TRAIN] Epoch 1 finished, loss=3224.171143, lr=3e-06 .\n",
      "2021-08-13 16:58:03 [INFO]\t[TRAIN] Epoch=2/100, Step=1/43, loss=41.635128, lr=5e-06, time_each_step=0.34s, eta=0:27:12\n",
      "2021-08-13 16:58:04 [INFO]\t[TRAIN] Epoch=2/100, Step=3/43, loss=30.43647, lr=6e-06, time_each_step=0.34s, eta=0:27:12\n",
      "2021-08-13 16:58:04 [INFO]\t[TRAIN] Epoch=2/100, Step=5/43, loss=23.010574, lr=6e-06, time_each_step=0.35s, eta=0:27:12\n",
      "2021-08-13 16:58:05 [INFO]\t[TRAIN] Epoch=2/100, Step=7/43, loss=30.107214, lr=6e-06, time_each_step=0.36s, eta=0:27:14\n",
      "2021-08-13 16:58:06 [INFO]\t[TRAIN] Epoch=2/100, Step=9/43, loss=55.712852, lr=6e-06, time_each_step=0.38s, eta=0:27:16\n",
      "2021-08-13 16:58:06 [INFO]\t[TRAIN] Epoch=2/100, Step=11/43, loss=22.563839, lr=7e-06, time_each_step=0.38s, eta=0:27:15\n",
      "2021-08-13 16:58:06 [INFO]\t[TRAIN] Epoch=2/100, Step=13/43, loss=33.434227, lr=7e-06, time_each_step=0.39s, eta=0:27:16\n",
      "2021-08-13 16:58:07 [INFO]\t[TRAIN] Epoch=2/100, Step=15/43, loss=30.781414, lr=7e-06, time_each_step=0.4s, eta=0:27:16\n",
      "2021-08-13 16:58:08 [INFO]\t[TRAIN] Epoch=2/100, Step=17/43, loss=27.788063, lr=7e-06, time_each_step=0.42s, eta=0:27:18\n",
      "2021-08-13 16:58:08 [INFO]\t[TRAIN] Epoch=2/100, Step=19/43, loss=40.859322, lr=8e-06, time_each_step=0.42s, eta=0:27:18\n",
      "2021-08-13 16:58:08 [INFO]\t[TRAIN] Epoch=2/100, Step=21/43, loss=25.219891, lr=8e-06, time_each_step=0.27s, eta=0:26:54\n",
      "2021-08-13 16:58:09 [INFO]\t[TRAIN] Epoch=2/100, Step=23/43, loss=25.172142, lr=8e-06, time_each_step=0.26s, eta=0:26:52\n",
      "2021-08-13 16:58:09 [INFO]\t[TRAIN] Epoch=2/100, Step=25/43, loss=17.882786, lr=8e-06, time_each_step=0.25s, eta=0:26:51\n",
      "2021-08-13 16:58:10 [INFO]\t[TRAIN] Epoch=2/100, Step=27/43, loss=22.312284, lr=9e-06, time_each_step=0.24s, eta=0:26:48\n",
      "2021-08-13 16:58:10 [INFO]\t[TRAIN] Epoch=2/100, Step=29/43, loss=22.065886, lr=9e-06, time_each_step=0.23s, eta=0:26:46\n",
      "2021-08-13 16:58:10 [INFO]\t[TRAIN] Epoch=2/100, Step=31/43, loss=21.447395, lr=9e-06, time_each_step=0.22s, eta=0:26:45\n",
      "2021-08-13 16:58:11 [INFO]\t[TRAIN] Epoch=2/100, Step=33/43, loss=31.813145, lr=9e-06, time_each_step=0.22s, eta=0:26:45\n",
      "2021-08-13 16:58:11 [INFO]\t[TRAIN] Epoch=2/100, Step=35/43, loss=22.326107, lr=1e-05, time_each_step=0.22s, eta=0:26:44\n",
      "2021-08-13 16:58:12 [INFO]\t[TRAIN] Epoch=2/100, Step=37/43, loss=19.33536, lr=1e-05, time_each_step=0.21s, eta=0:26:42\n",
      "2021-08-13 16:58:12 [INFO]\t[TRAIN] Epoch=2/100, Step=39/43, loss=27.964317, lr=1e-05, time_each_step=0.21s, eta=0:26:42\n",
      "2021-08-13 16:58:13 [INFO]\t[TRAIN] Epoch=2/100, Step=41/43, loss=25.614876, lr=1e-05, time_each_step=0.22s, eta=0:26:42\n",
      "2021-08-13 16:58:13 [INFO]\t[TRAIN] Epoch=2/100, Step=43/43, loss=20.419357, lr=1.1e-05, time_each_step=0.21s, eta=0:26:41\n",
      "2021-08-13 16:58:13 [INFO]\t[TRAIN] Epoch 2 finished, loss=27.018923, lr=8e-06 .\n",
      "2021-08-13 16:58:16 [INFO]\t[TRAIN] Epoch=3/100, Step=2/43, loss=20.444963, lr=1.1e-05, time_each_step=0.35s, eta=0:22:6\n",
      "2021-08-13 16:58:17 [INFO]\t[TRAIN] Epoch=3/100, Step=4/43, loss=20.823898, lr=1.1e-05, time_each_step=0.36s, eta=0:22:7\n",
      "2021-08-13 16:58:17 [INFO]\t[TRAIN] Epoch=3/100, Step=6/43, loss=30.280712, lr=1.1e-05, time_each_step=0.36s, eta=0:22:7\n",
      "2021-08-13 16:58:18 [INFO]\t[TRAIN] Epoch=3/100, Step=8/43, loss=29.394413, lr=1.2e-05, time_each_step=0.38s, eta=0:22:10\n",
      "2021-08-13 16:58:19 [INFO]\t[TRAIN] Epoch=3/100, Step=10/43, loss=24.973309, lr=1.2e-05, time_each_step=0.38s, eta=0:22:9\n",
      "2021-08-13 16:58:19 [INFO]\t[TRAIN] Epoch=3/100, Step=12/43, loss=22.535246, lr=1.2e-05, time_each_step=0.39s, eta=0:22:10\n",
      "2021-08-13 16:58:20 [INFO]\t[TRAIN] Epoch=3/100, Step=14/43, loss=20.531521, lr=1.2e-05, time_each_step=0.41s, eta=0:22:11\n",
      "2021-08-13 16:58:21 [INFO]\t[TRAIN] Epoch=3/100, Step=16/43, loss=33.749504, lr=1.3e-05, time_each_step=0.41s, eta=0:22:11\n",
      "2021-08-13 16:58:21 [INFO]\t[TRAIN] Epoch=3/100, Step=18/43, loss=29.152613, lr=1.3e-05, time_each_step=0.41s, eta=0:22:11\n",
      "2021-08-13 16:58:22 [INFO]\t[TRAIN] Epoch=3/100, Step=20/43, loss=32.537426, lr=1.3e-05, time_each_step=0.43s, eta=0:22:12\n",
      "2021-08-13 16:58:22 [INFO]\t[TRAIN] Epoch=3/100, Step=22/43, loss=29.483271, lr=1.3e-05, time_each_step=0.3s, eta=0:21:52\n",
      "2021-08-13 16:58:23 [INFO]\t[TRAIN] Epoch=3/100, Step=24/43, loss=19.483698, lr=1.4e-05, time_each_step=0.31s, eta=0:21:52\n",
      "2021-08-13 16:58:23 [INFO]\t[TRAIN] Epoch=3/100, Step=26/43, loss=25.326401, lr=1.4e-05, time_each_step=0.3s, eta=0:21:51\n",
      "2021-08-13 16:58:24 [INFO]\t[TRAIN] Epoch=3/100, Step=28/43, loss=18.946796, lr=1.4e-05, time_each_step=0.29s, eta=0:21:49\n",
      "2021-08-13 16:58:24 [INFO]\t[TRAIN] Epoch=3/100, Step=30/43, loss=15.276888, lr=1.4e-05, time_each_step=0.28s, eta=0:21:47\n",
      "2021-08-13 16:58:25 [INFO]\t[TRAIN] Epoch=3/100, Step=32/43, loss=23.824711, lr=1.5e-05, time_each_step=0.27s, eta=0:21:45\n",
      "2021-08-13 16:58:25 [INFO]\t[TRAIN] Epoch=3/100, Step=34/43, loss=22.581192, lr=1.5e-05, time_each_step=0.24s, eta=0:21:41\n",
      "2021-08-13 16:58:25 [INFO]\t[TRAIN] Epoch=3/100, Step=36/43, loss=21.119793, lr=1.5e-05, time_each_step=0.23s, eta=0:21:38\n",
      "2021-08-13 16:58:26 [INFO]\t[TRAIN] Epoch=3/100, Step=38/43, loss=18.501049, lr=1.5e-05, time_each_step=0.23s, eta=0:21:37\n",
      "2021-08-13 16:58:26 [INFO]\t[TRAIN] Epoch=3/100, Step=40/43, loss=27.093538, lr=1.6e-05, time_each_step=0.21s, eta=0:21:35\n",
      "2021-08-13 16:58:26 [INFO]\t[TRAIN] Epoch=3/100, Step=42/43, loss=29.538738, lr=1.6e-05, time_each_step=0.2s, eta=0:21:33\n",
      "2021-08-13 16:58:26 [INFO]\t[TRAIN] Epoch 3 finished, loss=24.523878, lr=1.3e-05 .\n",
      "2021-08-13 16:58:39 [INFO]\t[TRAIN] Epoch=4/100, Step=1/43, loss=22.416306, lr=1.6e-05, time_each_step=0.8s, eta=0:23:32\n",
      "2021-08-13 16:58:40 [INFO]\t[TRAIN] Epoch=4/100, Step=3/43, loss=22.062309, lr=1.6e-05, time_each_step=0.82s, eta=0:23:32\n",
      "2021-08-13 16:58:40 [INFO]\t[TRAIN] Epoch=4/100, Step=5/43, loss=18.376772, lr=1.7e-05, time_each_step=0.82s, eta=0:23:31\n",
      "2021-08-13 16:58:41 [INFO]\t[TRAIN] Epoch=4/100, Step=7/43, loss=27.462791, lr=1.7e-05, time_each_step=0.84s, eta=0:23:33\n",
      "2021-08-13 16:58:42 [INFO]\t[TRAIN] Epoch=4/100, Step=9/43, loss=20.7206, lr=1.7e-05, time_each_step=0.86s, eta=0:23:34\n",
      "2021-08-13 16:58:42 [INFO]\t[TRAIN] Epoch=4/100, Step=11/43, loss=21.100956, lr=1.7e-05, time_each_step=0.87s, eta=0:23:34\n",
      "2021-08-13 16:58:43 [INFO]\t[TRAIN] Epoch=4/100, Step=13/43, loss=26.660168, lr=1.8e-05, time_each_step=0.88s, eta=0:23:34\n",
      "2021-08-13 16:58:43 [INFO]\t[TRAIN] Epoch=4/100, Step=15/43, loss=28.049818, lr=1.8e-05, time_each_step=0.89s, eta=0:23:34\n",
      "2021-08-13 16:58:44 [INFO]\t[TRAIN] Epoch=4/100, Step=17/43, loss=19.994461, lr=1.8e-05, time_each_step=0.89s, eta=0:23:33\n",
      "2021-08-13 16:58:44 [INFO]\t[TRAIN] Epoch=4/100, Step=19/43, loss=20.922049, lr=1.8e-05, time_each_step=0.9s, eta=0:23:33\n",
      "2021-08-13 16:58:45 [INFO]\t[TRAIN] Epoch=4/100, Step=21/43, loss=16.223612, lr=1.9e-05, time_each_step=0.29s, eta=0:21:57\n",
      "2021-08-13 16:58:45 [INFO]\t[TRAIN] Epoch=4/100, Step=23/43, loss=17.307152, lr=1.9e-05, time_each_step=0.27s, eta=0:21:53\n",
      "2021-08-13 16:58:45 [INFO]\t[TRAIN] Epoch=4/100, Step=25/43, loss=21.158653, lr=1.9e-05, time_each_step=0.27s, eta=0:21:53\n",
      "2021-08-13 16:58:46 [INFO]\t[TRAIN] Epoch=4/100, Step=27/43, loss=28.234545, lr=1.9e-05, time_each_step=0.25s, eta=0:21:50\n",
      "2021-08-13 16:58:46 [INFO]\t[TRAIN] Epoch=4/100, Step=29/43, loss=17.75399, lr=2e-05, time_each_step=0.24s, eta=0:21:48\n",
      "2021-08-13 16:58:47 [INFO]\t[TRAIN] Epoch=4/100, Step=31/43, loss=18.764084, lr=2e-05, time_each_step=0.23s, eta=0:21:46\n",
      "2021-08-13 16:58:47 [INFO]\t[TRAIN] Epoch=4/100, Step=33/43, loss=42.879433, lr=2e-05, time_each_step=0.23s, eta=0:21:45\n",
      "2021-08-13 16:58:48 [INFO]\t[TRAIN] Epoch=4/100, Step=35/43, loss=22.605541, lr=2e-05, time_each_step=0.22s, eta=0:21:43\n",
      "2021-08-13 16:58:48 [INFO]\t[TRAIN] Epoch=4/100, Step=37/43, loss=19.344282, lr=2.1e-05, time_each_step=0.22s, eta=0:21:43\n",
      "2021-08-13 16:58:49 [INFO]\t[TRAIN] Epoch=4/100, Step=39/43, loss=16.363205, lr=2.1e-05, time_each_step=0.21s, eta=0:21:42\n",
      "2021-08-13 16:58:49 [INFO]\t[TRAIN] Epoch=4/100, Step=41/43, loss=26.022739, lr=2.1e-05, time_each_step=0.22s, eta=0:21:42\n",
      "2021-08-13 16:58:49 [INFO]\t[TRAIN] Epoch=4/100, Step=43/43, loss=16.80323, lr=2.1e-05, time_each_step=0.22s, eta=0:21:42\n",
      "2021-08-13 16:58:49 [INFO]\t[TRAIN] Epoch 4 finished, loss=21.923803, lr=1.9e-05 .\n",
      "2021-08-13 16:58:54 [INFO]\t[TRAIN] Epoch=5/100, Step=2/43, loss=16.766609, lr=2.2e-05, time_each_step=0.42s, eta=0:37:38\n",
      "2021-08-13 16:58:55 [INFO]\t[TRAIN] Epoch=5/100, Step=4/43, loss=21.988607, lr=2.2e-05, time_each_step=0.43s, eta=0:37:40\n",
      "2021-08-13 16:58:55 [INFO]\t[TRAIN] Epoch=5/100, Step=6/43, loss=16.308578, lr=2.2e-05, time_each_step=0.45s, eta=0:37:41\n",
      "2021-08-13 16:58:56 [INFO]\t[TRAIN] Epoch=5/100, Step=8/43, loss=19.306026, lr=2.2e-05, time_each_step=0.47s, eta=0:37:45\n",
      "2021-08-13 16:58:57 [INFO]\t[TRAIN] Epoch=5/100, Step=10/43, loss=20.267101, lr=2.3e-05, time_each_step=0.48s, eta=0:37:44\n",
      "2021-08-13 16:58:57 [INFO]\t[TRAIN] Epoch=5/100, Step=12/43, loss=29.162384, lr=2.3e-05, time_each_step=0.48s, eta=0:37:44\n",
      "2021-08-13 16:58:58 [INFO]\t[TRAIN] Epoch=5/100, Step=14/43, loss=17.840729, lr=2.3e-05, time_each_step=0.48s, eta=0:37:43\n",
      "2021-08-13 16:58:58 [INFO]\t[TRAIN] Epoch=5/100, Step=16/43, loss=19.251465, lr=2.3e-05, time_each_step=0.5s, eta=0:37:45\n",
      "2021-08-13 16:58:59 [INFO]\t[TRAIN] Epoch=5/100, Step=18/43, loss=26.909029, lr=2.4e-05, time_each_step=0.5s, eta=0:37:44\n",
      "2021-08-13 16:58:59 [INFO]\t[TRAIN] Epoch=5/100, Step=20/43, loss=19.991322, lr=2.4e-05, time_each_step=0.5s, eta=0:37:43\n",
      "2021-08-13 16:59:00 [INFO]\t[TRAIN] Epoch=5/100, Step=22/43, loss=27.571518, lr=2.4e-05, time_each_step=0.3s, eta=0:37:12\n",
      "2021-08-13 16:59:00 [INFO]\t[TRAIN] Epoch=5/100, Step=24/43, loss=17.271828, lr=2.4e-05, time_each_step=0.28s, eta=0:37:9\n",
      "2021-08-13 16:59:01 [INFO]\t[TRAIN] Epoch=5/100, Step=26/43, loss=27.838985, lr=2.5e-05, time_each_step=0.27s, eta=0:37:6\n",
      "2021-08-13 16:59:01 [INFO]\t[TRAIN] Epoch=5/100, Step=28/43, loss=28.122887, lr=2.5e-05, time_each_step=0.25s, eta=0:37:3\n",
      "2021-08-13 16:59:02 [INFO]\t[TRAIN] Epoch=5/100, Step=30/43, loss=18.468266, lr=2.5e-05, time_each_step=0.24s, eta=0:37:1\n",
      "2021-08-13 16:59:02 [INFO]\t[TRAIN] Epoch=5/100, Step=32/43, loss=19.663612, lr=2.5e-05, time_each_step=0.23s, eta=0:36:59\n",
      "2021-08-13 16:59:02 [INFO]\t[TRAIN] Epoch=5/100, Step=34/43, loss=23.569746, lr=2.6e-05, time_each_step=0.23s, eta=0:36:59\n",
      "2021-08-13 16:59:03 [INFO]\t[TRAIN] Epoch=5/100, Step=36/43, loss=17.44594, lr=2.6e-05, time_each_step=0.21s, eta=0:36:56\n",
      "2021-08-13 16:59:03 [INFO]\t[TRAIN] Epoch=5/100, Step=38/43, loss=17.61931, lr=2.6e-05, time_each_step=0.22s, eta=0:36:56\n",
      "2021-08-13 16:59:04 [INFO]\t[TRAIN] Epoch=5/100, Step=40/43, loss=15.425895, lr=2.6e-05, time_each_step=0.22s, eta=0:36:56\n",
      "2021-08-13 16:59:04 [INFO]\t[TRAIN] Epoch=5/100, Step=42/43, loss=28.621328, lr=2.7e-05, time_each_step=0.22s, eta=0:36:56\n",
      "2021-08-13 16:59:05 [INFO]\t[TRAIN] Epoch 5 finished, loss=22.045197, lr=2.4e-05 .\n",
      "2021-08-13 16:59:11 [INFO]\t[TRAIN] Epoch=6/100, Step=1/43, loss=19.681738, lr=2.7e-05, time_each_step=0.51s, eta=0:25:11\n",
      "2021-08-13 16:59:11 [INFO]\t[TRAIN] Epoch=6/100, Step=3/43, loss=18.684696, lr=2.7e-05, time_each_step=0.53s, eta=0:25:14\n",
      "2021-08-13 16:59:12 [INFO]\t[TRAIN] Epoch=6/100, Step=5/43, loss=19.794605, lr=2.7e-05, time_each_step=0.54s, eta=0:25:13\n",
      "2021-08-13 16:59:13 [INFO]\t[TRAIN] Epoch=6/100, Step=7/43, loss=17.543941, lr=2.8e-05, time_each_step=0.56s, eta=0:25:15\n",
      "2021-08-13 16:59:13 [INFO]\t[TRAIN] Epoch=6/100, Step=9/43, loss=18.513956, lr=2.8e-05, time_each_step=0.57s, eta=0:25:17\n",
      "2021-08-13 16:59:14 [INFO]\t[TRAIN] Epoch=6/100, Step=11/43, loss=26.457352, lr=2.8e-05, time_each_step=0.57s, eta=0:25:16\n",
      "2021-08-13 16:59:14 [INFO]\t[TRAIN] Epoch=6/100, Step=13/43, loss=17.082611, lr=2.8e-05, time_each_step=0.59s, eta=0:25:18\n",
      "2021-08-13 16:59:15 [INFO]\t[TRAIN] Epoch=6/100, Step=15/43, loss=29.273201, lr=2.9e-05, time_each_step=0.59s, eta=0:25:16\n",
      "2021-08-13 16:59:16 [INFO]\t[TRAIN] Epoch=6/100, Step=17/43, loss=15.9613, lr=2.9e-05, time_each_step=0.6s, eta=0:25:17\n",
      "2021-08-13 16:59:16 [INFO]\t[TRAIN] Epoch=6/100, Step=19/43, loss=16.211596, lr=2.9e-05, time_each_step=0.6s, eta=0:25:15\n",
      "2021-08-13 16:59:17 [INFO]\t[TRAIN] Epoch=6/100, Step=21/43, loss=32.509918, lr=2.9e-05, time_each_step=0.31s, eta=0:24:30\n",
      "2021-08-13 16:59:17 [INFO]\t[TRAIN] Epoch=6/100, Step=23/43, loss=17.750856, lr=3e-05, time_each_step=0.28s, eta=0:24:26\n",
      "2021-08-13 16:59:17 [INFO]\t[TRAIN] Epoch=6/100, Step=25/43, loss=17.081768, lr=3e-05, time_each_step=0.27s, eta=0:24:23\n",
      "2021-08-13 16:59:18 [INFO]\t[TRAIN] Epoch=6/100, Step=27/43, loss=18.638979, lr=3e-05, time_each_step=0.26s, eta=0:24:21\n",
      "2021-08-13 16:59:18 [INFO]\t[TRAIN] Epoch=6/100, Step=29/43, loss=20.907848, lr=3e-05, time_each_step=0.25s, eta=0:24:19\n",
      "2021-08-13 16:59:19 [INFO]\t[TRAIN] Epoch=6/100, Step=31/43, loss=20.453274, lr=3.1e-05, time_each_step=0.24s, eta=0:24:17\n",
      "2021-08-13 16:59:19 [INFO]\t[TRAIN] Epoch=6/100, Step=33/43, loss=14.561399, lr=3.1e-05, time_each_step=0.23s, eta=0:24:15\n",
      "2021-08-13 16:59:19 [INFO]\t[TRAIN] Epoch=6/100, Step=35/43, loss=15.824587, lr=3.1e-05, time_each_step=0.22s, eta=0:24:13\n",
      "2021-08-13 16:59:20 [INFO]\t[TRAIN] Epoch=6/100, Step=37/43, loss=19.641449, lr=3.1e-05, time_each_step=0.21s, eta=0:24:11\n",
      "2021-08-13 16:59:20 [INFO]\t[TRAIN] Epoch=6/100, Step=39/43, loss=26.507685, lr=3.2e-05, time_each_step=0.21s, eta=0:24:11\n",
      "2021-08-13 16:59:21 [INFO]\t[TRAIN] Epoch=6/100, Step=41/43, loss=29.631247, lr=3.2e-05, time_each_step=0.21s, eta=0:24:11\n",
      "2021-08-13 16:59:21 [INFO]\t[TRAIN] Epoch=6/100, Step=43/43, loss=22.300024, lr=3.2e-05, time_each_step=0.21s, eta=0:24:11\n",
      "2021-08-13 16:59:21 [INFO]\t[TRAIN] Epoch 6 finished, loss=20.852324, lr=3e-05 .\n",
      "2021-08-13 16:59:26 [INFO]\t[TRAIN] Epoch=7/100, Step=2/43, loss=18.050406, lr=3.2e-05, time_each_step=0.42s, eta=0:27:19\n",
      "2021-08-13 16:59:26 [INFO]\t[TRAIN] Epoch=7/100, Step=4/43, loss=22.338289, lr=3.3e-05, time_each_step=0.43s, eta=0:27:19\n",
      "2021-08-13 16:59:27 [INFO]\t[TRAIN] Epoch=7/100, Step=6/43, loss=24.194441, lr=3.3e-05, time_each_step=0.45s, eta=0:27:22\n",
      "2021-08-13 16:59:28 [INFO]\t[TRAIN] Epoch=7/100, Step=8/43, loss=21.538868, lr=3.3e-05, time_each_step=0.46s, eta=0:27:23\n",
      "2021-08-13 16:59:28 [INFO]\t[TRAIN] Epoch=7/100, Step=10/43, loss=26.951096, lr=3.3e-05, time_each_step=0.47s, eta=0:27:23\n",
      "2021-08-13 16:59:29 [INFO]\t[TRAIN] Epoch=7/100, Step=12/43, loss=16.969078, lr=3.4e-05, time_each_step=0.5s, eta=0:27:28\n",
      "2021-08-13 16:59:30 [INFO]\t[TRAIN] Epoch=7/100, Step=14/43, loss=19.173607, lr=3.4e-05, time_each_step=0.51s, eta=0:27:27\n",
      "2021-08-13 16:59:31 [INFO]\t[TRAIN] Epoch=7/100, Step=16/43, loss=18.435022, lr=3.4e-05, time_each_step=0.51s, eta=0:27:26\n",
      "2021-08-13 16:59:31 [INFO]\t[TRAIN] Epoch=7/100, Step=18/43, loss=15.693926, lr=3.4e-05, time_each_step=0.51s, eta=0:27:25\n",
      "2021-08-13 16:59:32 [INFO]\t[TRAIN] Epoch=7/100, Step=20/43, loss=21.303448, lr=3.5e-05, time_each_step=0.51s, eta=0:27:24\n",
      "2021-08-13 16:59:32 [INFO]\t[TRAIN] Epoch=7/100, Step=22/43, loss=14.411936, lr=3.5e-05, time_each_step=0.31s, eta=0:26:54\n",
      "2021-08-13 16:59:33 [INFO]\t[TRAIN] Epoch=7/100, Step=24/43, loss=19.647657, lr=3.5e-05, time_each_step=0.31s, eta=0:26:52\n",
      "2021-08-13 16:59:33 [INFO]\t[TRAIN] Epoch=7/100, Step=26/43, loss=28.525009, lr=3.5e-05, time_each_step=0.29s, eta=0:26:48\n",
      "2021-08-13 16:59:34 [INFO]\t[TRAIN] Epoch=7/100, Step=28/43, loss=27.37281, lr=3.6e-05, time_each_step=0.29s, eta=0:26:48\n",
      "2021-08-13 16:59:34 [INFO]\t[TRAIN] Epoch=7/100, Step=30/43, loss=18.385853, lr=3.6e-05, time_each_step=0.28s, eta=0:26:46\n",
      "2021-08-13 16:59:34 [INFO]\t[TRAIN] Epoch=7/100, Step=32/43, loss=15.602081, lr=3.6e-05, time_each_step=0.25s, eta=0:26:41\n",
      "2021-08-13 16:59:35 [INFO]\t[TRAIN] Epoch=7/100, Step=34/43, loss=18.718876, lr=3.6e-05, time_each_step=0.24s, eta=0:26:40\n",
      "2021-08-13 16:59:35 [INFO]\t[TRAIN] Epoch=7/100, Step=36/43, loss=15.533276, lr=3.7e-05, time_each_step=0.25s, eta=0:26:41\n",
      "2021-08-13 16:59:36 [INFO]\t[TRAIN] Epoch=7/100, Step=38/43, loss=31.981867, lr=3.7e-05, time_each_step=0.24s, eta=0:26:38\n",
      "2021-08-13 16:59:36 [INFO]\t[TRAIN] Epoch=7/100, Step=40/43, loss=17.851585, lr=3.7e-05, time_each_step=0.24s, eta=0:26:38\n",
      "2021-08-13 16:59:37 [INFO]\t[TRAIN] Epoch=7/100, Step=42/43, loss=22.232121, lr=3.7e-05, time_each_step=0.23s, eta=0:26:37\n",
      "2021-08-13 16:59:37 [INFO]\t[TRAIN] Epoch 7 finished, loss=19.103897, lr=3.5e-05 .\n",
      "2021-08-13 16:59:43 [INFO]\t[TRAIN] Epoch=8/100, Step=1/43, loss=18.479994, lr=3.8e-05, time_each_step=0.53s, eta=0:25:30\n",
      "2021-08-13 16:59:44 [INFO]\t[TRAIN] Epoch=8/100, Step=3/43, loss=12.836681, lr=3.8e-05, time_each_step=0.54s, eta=0:25:30\n",
      "2021-08-13 16:59:45 [INFO]\t[TRAIN] Epoch=8/100, Step=5/43, loss=22.705462, lr=3.8e-05, time_each_step=0.54s, eta=0:25:30\n",
      "2021-08-13 16:59:45 [INFO]\t[TRAIN] Epoch=8/100, Step=7/43, loss=16.739, lr=3.8e-05, time_each_step=0.55s, eta=0:25:31\n",
      "2021-08-13 16:59:46 [INFO]\t[TRAIN] Epoch=8/100, Step=9/43, loss=18.646416, lr=3.9e-05, time_each_step=0.58s, eta=0:25:34\n",
      "2021-08-13 16:59:46 [INFO]\t[TRAIN] Epoch=8/100, Step=11/43, loss=20.230736, lr=3.9e-05, time_each_step=0.58s, eta=0:25:32\n",
      "2021-08-13 16:59:47 [INFO]\t[TRAIN] Epoch=8/100, Step=13/43, loss=16.127953, lr=3.9e-05, time_each_step=0.57s, eta=0:25:30\n",
      "2021-08-13 16:59:48 [INFO]\t[TRAIN] Epoch=8/100, Step=15/43, loss=20.262598, lr=3.9e-05, time_each_step=0.59s, eta=0:25:32\n",
      "2021-08-13 16:59:48 [INFO]\t[TRAIN] Epoch=8/100, Step=17/43, loss=11.824078, lr=4e-05, time_each_step=0.6s, eta=0:25:32\n",
      "2021-08-13 16:59:49 [INFO]\t[TRAIN] Epoch=8/100, Step=19/43, loss=15.143897, lr=4e-05, time_each_step=0.61s, eta=0:25:33\n",
      "2021-08-13 16:59:50 [INFO]\t[TRAIN] Epoch=8/100, Step=21/43, loss=18.263575, lr=4e-05, time_each_step=0.32s, eta=0:24:48\n",
      "2021-08-13 16:59:50 [INFO]\t[TRAIN] Epoch=8/100, Step=23/43, loss=18.64139, lr=4e-05, time_each_step=0.32s, eta=0:24:48\n",
      "2021-08-13 16:59:51 [INFO]\t[TRAIN] Epoch=8/100, Step=25/43, loss=26.76771, lr=4.1e-05, time_each_step=0.32s, eta=0:24:47\n",
      "2021-08-13 16:59:51 [INFO]\t[TRAIN] Epoch=8/100, Step=27/43, loss=19.161858, lr=4.1e-05, time_each_step=0.32s, eta=0:24:46\n",
      "2021-08-13 16:59:52 [INFO]\t[TRAIN] Epoch=8/100, Step=29/43, loss=25.507523, lr=4.1e-05, time_each_step=0.3s, eta=0:24:43\n",
      "2021-08-13 16:59:53 [INFO]\t[TRAIN] Epoch=8/100, Step=31/43, loss=14.498831, lr=4.1e-05, time_each_step=0.32s, eta=0:24:44\n",
      "2021-08-13 16:59:53 [INFO]\t[TRAIN] Epoch=8/100, Step=33/43, loss=16.63542, lr=4.2e-05, time_each_step=0.32s, eta=0:24:43\n",
      "2021-08-13 16:59:54 [INFO]\t[TRAIN] Epoch=8/100, Step=35/43, loss=22.973492, lr=4.2e-05, time_each_step=0.31s, eta=0:24:41\n",
      "2021-08-13 16:59:54 [INFO]\t[TRAIN] Epoch=8/100, Step=37/43, loss=18.188049, lr=4.2e-05, time_each_step=0.29s, eta=0:24:38\n",
      "2021-08-13 16:59:54 [INFO]\t[TRAIN] Epoch=8/100, Step=39/43, loss=25.959019, lr=4.2e-05, time_each_step=0.26s, eta=0:24:34\n",
      "2021-08-13 16:59:55 [INFO]\t[TRAIN] Epoch=8/100, Step=41/43, loss=16.039507, lr=4.3e-05, time_each_step=0.27s, eta=0:24:34\n",
      "2021-08-13 16:59:55 [INFO]\t[TRAIN] Epoch=8/100, Step=43/43, loss=16.6628, lr=4.3e-05, time_each_step=0.25s, eta=0:24:32\n",
      "2021-08-13 16:59:55 [INFO]\t[TRAIN] Epoch 8 finished, loss=19.079542, lr=4e-05 .\n",
      "2021-08-13 17:00:00 [INFO]\t[TRAIN] Epoch=9/100, Step=2/43, loss=15.058687, lr=4.3e-05, time_each_step=0.44s, eta=0:29:3\n",
      "2021-08-13 17:00:00 [INFO]\t[TRAIN] Epoch=9/100, Step=4/43, loss=10.361157, lr=4.3e-05, time_each_step=0.45s, eta=0:29:5\n",
      "2021-08-13 17:00:01 [INFO]\t[TRAIN] Epoch=9/100, Step=6/43, loss=13.0089, lr=4.4e-05, time_each_step=0.45s, eta=0:29:4\n",
      "2021-08-13 17:00:02 [INFO]\t[TRAIN] Epoch=9/100, Step=8/43, loss=13.518887, lr=4.4e-05, time_each_step=0.44s, eta=0:29:2\n",
      "2021-08-13 17:00:02 [INFO]\t[TRAIN] Epoch=9/100, Step=10/43, loss=13.568211, lr=4.4e-05, time_each_step=0.44s, eta=0:29:1\n",
      "2021-08-13 17:00:02 [INFO]\t[TRAIN] Epoch=9/100, Step=12/43, loss=23.479748, lr=4.4e-05, time_each_step=0.43s, eta=0:28:59\n",
      "2021-08-13 17:00:03 [INFO]\t[TRAIN] Epoch=9/100, Step=14/43, loss=17.417145, lr=4.5e-05, time_each_step=0.45s, eta=0:29:1\n",
      "2021-08-13 17:00:04 [INFO]\t[TRAIN] Epoch=9/100, Step=16/43, loss=24.48457, lr=4.5e-05, time_each_step=0.48s, eta=0:29:4\n",
      "2021-08-13 17:00:05 [INFO]\t[TRAIN] Epoch=9/100, Step=18/43, loss=24.53072, lr=4.5e-05, time_each_step=0.48s, eta=0:29:3\n",
      "2021-08-13 17:00:05 [INFO]\t[TRAIN] Epoch=9/100, Step=20/43, loss=17.251266, lr=4.5e-05, time_each_step=0.5s, eta=0:29:6\n",
      "2021-08-13 17:00:06 [INFO]\t[TRAIN] Epoch=9/100, Step=22/43, loss=17.683769, lr=4.6e-05, time_each_step=0.31s, eta=0:28:35\n",
      "2021-08-13 17:00:06 [INFO]\t[TRAIN] Epoch=9/100, Step=24/43, loss=17.187859, lr=4.6e-05, time_each_step=0.3s, eta=0:28:33\n",
      "2021-08-13 17:00:07 [INFO]\t[TRAIN] Epoch=9/100, Step=26/43, loss=21.16222, lr=4.6e-05, time_each_step=0.28s, eta=0:28:30\n",
      "2021-08-13 17:00:07 [INFO]\t[TRAIN] Epoch=9/100, Step=28/43, loss=19.778069, lr=4.6e-05, time_each_step=0.27s, eta=0:28:27\n",
      "2021-08-13 17:00:07 [INFO]\t[TRAIN] Epoch=9/100, Step=30/43, loss=18.737299, lr=4.7e-05, time_each_step=0.26s, eta=0:28:26\n",
      "2021-08-13 17:00:08 [INFO]\t[TRAIN] Epoch=9/100, Step=32/43, loss=16.721375, lr=4.7e-05, time_each_step=0.27s, eta=0:28:27\n",
      "2021-08-13 17:00:08 [INFO]\t[TRAIN] Epoch=9/100, Step=34/43, loss=12.505671, lr=4.7e-05, time_each_step=0.26s, eta=0:28:25\n",
      "2021-08-13 17:00:09 [INFO]\t[TRAIN] Epoch=9/100, Step=36/43, loss=20.038776, lr=4.7e-05, time_each_step=0.24s, eta=0:28:21\n",
      "2021-08-13 17:00:09 [INFO]\t[TRAIN] Epoch=9/100, Step=38/43, loss=17.763725, lr=4.8e-05, time_each_step=0.23s, eta=0:28:19\n",
      "2021-08-13 17:00:09 [INFO]\t[TRAIN] Epoch=9/100, Step=40/43, loss=19.726669, lr=4.8e-05, time_each_step=0.2s, eta=0:28:15\n",
      "2021-08-13 17:00:10 [INFO]\t[TRAIN] Epoch=9/100, Step=42/43, loss=14.069138, lr=4.8e-05, time_each_step=0.19s, eta=0:28:14\n",
      "2021-08-13 17:00:10 [INFO]\t[TRAIN] Epoch 9 finished, loss=17.891592, lr=4.6e-05 .\n",
      "2021-08-13 17:00:14 [INFO]\t[TRAIN] Epoch=10/100, Step=1/43, loss=18.355591, lr=4.8e-05, time_each_step=0.37s, eta=0:23:1\n",
      "2021-08-13 17:00:15 [INFO]\t[TRAIN] Epoch=10/100, Step=3/43, loss=19.485939, lr=4.9e-05, time_each_step=0.41s, eta=0:23:6\n",
      "2021-08-13 17:00:16 [INFO]\t[TRAIN] Epoch=10/100, Step=5/43, loss=20.117996, lr=4.9e-05, time_each_step=0.43s, eta=0:23:9\n",
      "2021-08-13 17:00:16 [INFO]\t[TRAIN] Epoch=10/100, Step=7/43, loss=14.119319, lr=4.9e-05, time_each_step=0.45s, eta=0:23:11\n",
      "2021-08-13 17:00:17 [INFO]\t[TRAIN] Epoch=10/100, Step=9/43, loss=20.2715, lr=4.9e-05, time_each_step=0.45s, eta=0:23:10\n",
      "2021-08-13 17:00:17 [INFO]\t[TRAIN] Epoch=10/100, Step=11/43, loss=12.562015, lr=5e-05, time_each_step=0.45s, eta=0:23:10\n",
      "2021-08-13 17:00:18 [INFO]\t[TRAIN] Epoch=10/100, Step=13/43, loss=14.643541, lr=5e-05, time_each_step=0.47s, eta=0:23:11\n",
      "2021-08-13 17:00:19 [INFO]\t[TRAIN] Epoch=10/100, Step=15/43, loss=14.802586, lr=5e-05, time_each_step=0.49s, eta=0:23:14\n",
      "2021-08-13 17:00:20 [INFO]\t[TRAIN] Epoch=10/100, Step=17/43, loss=18.356415, lr=5e-05, time_each_step=0.52s, eta=0:23:17\n",
      "2021-08-13 17:00:20 [INFO]\t[TRAIN] Epoch=10/100, Step=19/43, loss=17.430809, lr=5.1e-05, time_each_step=0.53s, eta=0:23:17\n",
      "2021-08-13 17:00:21 [INFO]\t[TRAIN] Epoch=10/100, Step=21/43, loss=17.954811, lr=5.1e-05, time_each_step=0.35s, eta=0:22:50\n",
      "2021-08-13 17:00:21 [INFO]\t[TRAIN] Epoch=10/100, Step=23/43, loss=19.981709, lr=5.1e-05, time_each_step=0.32s, eta=0:22:44\n",
      "2021-08-13 17:00:22 [INFO]\t[TRAIN] Epoch=10/100, Step=25/43, loss=21.475601, lr=5.1e-05, time_each_step=0.3s, eta=0:22:40\n",
      "2021-08-13 17:00:22 [INFO]\t[TRAIN] Epoch=10/100, Step=27/43, loss=16.362225, lr=5.2e-05, time_each_step=0.29s, eta=0:22:39\n",
      "2021-08-13 17:00:23 [INFO]\t[TRAIN] Epoch=10/100, Step=29/43, loss=18.576506, lr=5.2e-05, time_each_step=0.28s, eta=0:22:37\n",
      "2021-08-13 17:00:23 [INFO]\t[TRAIN] Epoch=10/100, Step=31/43, loss=16.736504, lr=5.2e-05, time_each_step=0.29s, eta=0:22:38\n",
      "2021-08-13 17:00:24 [INFO]\t[TRAIN] Epoch=10/100, Step=33/43, loss=28.78965, lr=5.2e-05, time_each_step=0.28s, eta=0:22:36\n",
      "2021-08-13 17:00:24 [INFO]\t[TRAIN] Epoch=10/100, Step=35/43, loss=26.502045, lr=5.3e-05, time_each_step=0.26s, eta=0:22:33\n",
      "2021-08-13 17:00:25 [INFO]\t[TRAIN] Epoch=10/100, Step=37/43, loss=16.333555, lr=5.3e-05, time_each_step=0.24s, eta=0:22:29\n",
      "2021-08-13 17:00:25 [INFO]\t[TRAIN] Epoch=10/100, Step=39/43, loss=15.396358, lr=5.3e-05, time_each_step=0.23s, eta=0:22:28\n",
      "2021-08-13 17:00:26 [INFO]\t[TRAIN] Epoch=10/100, Step=41/43, loss=15.097067, lr=5.3e-05, time_each_step=0.23s, eta=0:22:27\n",
      "2021-08-13 17:00:26 [INFO]\t[TRAIN] Epoch=10/100, Step=43/43, loss=15.494947, lr=5.4e-05, time_each_step=0.23s, eta=0:22:27\n",
      "2021-08-13 17:00:26 [INFO]\t[TRAIN] Epoch 10 finished, loss=17.875082, lr=5.1e-05 .\n",
      "2021-08-13 17:00:26 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:06<00:00,  2.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:00:32 [INFO]\t[EVAL] Finished, Epoch=10, bbox_map=4.545455 .\n",
      "2021-08-13 17:00:34 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:00:35 [INFO]\tModel saved in output/yolov3_darknet53/epoch_10.\n",
      "2021-08-13 17:00:35 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_10, bbox_map=4.545454545454546\n",
      "2021-08-13 17:00:39 [INFO]\t[TRAIN] Epoch=11/100, Step=2/43, loss=16.630821, lr=5.4e-05, time_each_step=0.43s, eta=0:25:18\n",
      "2021-08-13 17:00:41 [INFO]\t[TRAIN] Epoch=11/100, Step=4/43, loss=15.17447, lr=5.4e-05, time_each_step=0.46s, eta=0:25:18\n",
      "2021-08-13 17:00:41 [INFO]\t[TRAIN] Epoch=11/100, Step=6/43, loss=16.393438, lr=5.4e-05, time_each_step=0.46s, eta=0:25:18\n",
      "2021-08-13 17:00:42 [INFO]\t[TRAIN] Epoch=11/100, Step=8/43, loss=13.245962, lr=5.5e-05, time_each_step=0.48s, eta=0:25:17\n",
      "2021-08-13 17:00:43 [INFO]\t[TRAIN] Epoch=11/100, Step=10/43, loss=17.297863, lr=5.5e-05, time_each_step=0.49s, eta=0:25:17\n",
      "2021-08-13 17:00:43 [INFO]\t[TRAIN] Epoch=11/100, Step=12/43, loss=20.004856, lr=5.5e-05, time_each_step=0.5s, eta=0:25:16\n",
      "2021-08-13 17:00:44 [INFO]\t[TRAIN] Epoch=11/100, Step=14/43, loss=13.675212, lr=5.5e-05, time_each_step=0.51s, eta=0:25:15\n",
      "2021-08-13 17:00:45 [INFO]\t[TRAIN] Epoch=11/100, Step=16/43, loss=17.022053, lr=5.6e-05, time_each_step=0.52s, eta=0:25:15\n",
      "2021-08-13 17:00:45 [INFO]\t[TRAIN] Epoch=11/100, Step=18/43, loss=23.617474, lr=5.6e-05, time_each_step=0.54s, eta=0:25:14\n",
      "2021-08-13 17:00:46 [INFO]\t[TRAIN] Epoch=11/100, Step=20/43, loss=11.836985, lr=5.6e-05, time_each_step=0.53s, eta=0:25:13\n",
      "2021-08-13 17:00:46 [INFO]\t[TRAIN] Epoch=11/100, Step=22/43, loss=19.343079, lr=5.6e-05, time_each_step=0.34s, eta=0:25:8\n",
      "2021-08-13 17:00:47 [INFO]\t[TRAIN] Epoch=11/100, Step=24/43, loss=23.189901, lr=5.7e-05, time_each_step=0.31s, eta=0:25:6\n",
      "2021-08-13 17:00:47 [INFO]\t[TRAIN] Epoch=11/100, Step=26/43, loss=22.80481, lr=5.7e-05, time_each_step=0.3s, eta=0:25:6\n",
      "2021-08-13 17:00:47 [INFO]\t[TRAIN] Epoch=11/100, Step=28/43, loss=20.842812, lr=5.7e-05, time_each_step=0.27s, eta=0:25:5\n",
      "2021-08-13 17:00:48 [INFO]\t[TRAIN] Epoch=11/100, Step=30/43, loss=16.704681, lr=5.7e-05, time_each_step=0.25s, eta=0:25:4\n",
      "2021-08-13 17:00:48 [INFO]\t[TRAIN] Epoch=11/100, Step=32/43, loss=17.036835, lr=5.8e-05, time_each_step=0.23s, eta=0:25:3\n",
      "2021-08-13 17:00:48 [INFO]\t[TRAIN] Epoch=11/100, Step=34/43, loss=11.859403, lr=5.8e-05, time_each_step=0.22s, eta=0:25:3\n",
      "2021-08-13 17:00:49 [INFO]\t[TRAIN] Epoch=11/100, Step=36/43, loss=17.196726, lr=5.8e-05, time_each_step=0.21s, eta=0:25:2\n",
      "2021-08-13 17:00:49 [INFO]\t[TRAIN] Epoch=11/100, Step=38/43, loss=14.248964, lr=5.8e-05, time_each_step=0.19s, eta=0:25:2\n",
      "2021-08-13 17:00:50 [INFO]\t[TRAIN] Epoch=11/100, Step=40/43, loss=14.934505, lr=5.9e-05, time_each_step=0.19s, eta=0:25:1\n",
      "2021-08-13 17:00:50 [INFO]\t[TRAIN] Epoch=11/100, Step=42/43, loss=18.126888, lr=5.9e-05, time_each_step=0.2s, eta=0:25:1\n",
      "2021-08-13 17:00:51 [INFO]\t[TRAIN] Epoch 11 finished, loss=17.137987, lr=5.6e-05 .\n",
      "2021-08-13 17:00:56 [INFO]\t[TRAIN] Epoch=12/100, Step=1/43, loss=13.303488, lr=5.9e-05, time_each_step=0.44s, eta=0:24:17\n",
      "2021-08-13 17:00:57 [INFO]\t[TRAIN] Epoch=12/100, Step=3/43, loss=18.545849, lr=5.9e-05, time_each_step=0.48s, eta=0:24:18\n",
      "2021-08-13 17:00:57 [INFO]\t[TRAIN] Epoch=12/100, Step=5/43, loss=12.967278, lr=6e-05, time_each_step=0.49s, eta=0:24:17\n",
      "2021-08-13 17:00:58 [INFO]\t[TRAIN] Epoch=12/100, Step=7/43, loss=14.167674, lr=6e-05, time_each_step=0.5s, eta=0:24:16\n",
      "2021-08-13 17:00:58 [INFO]\t[TRAIN] Epoch=12/100, Step=9/43, loss=13.858908, lr=6e-05, time_each_step=0.52s, eta=0:24:16\n",
      "2021-08-13 17:00:59 [INFO]\t[TRAIN] Epoch=12/100, Step=11/43, loss=21.980522, lr=6e-05, time_each_step=0.54s, eta=0:24:16\n",
      "2021-08-13 17:01:00 [INFO]\t[TRAIN] Epoch=12/100, Step=13/43, loss=22.404285, lr=6.1e-05, time_each_step=0.54s, eta=0:24:15\n",
      "2021-08-13 17:01:00 [INFO]\t[TRAIN] Epoch=12/100, Step=15/43, loss=22.702326, lr=6.1e-05, time_each_step=0.53s, eta=0:24:13\n",
      "2021-08-13 17:01:00 [INFO]\t[TRAIN] Epoch=12/100, Step=17/43, loss=12.434698, lr=6.1e-05, time_each_step=0.54s, eta=0:24:12\n",
      "2021-08-13 17:01:01 [INFO]\t[TRAIN] Epoch=12/100, Step=19/43, loss=26.022903, lr=6.1e-05, time_each_step=0.54s, eta=0:24:11\n",
      "2021-08-13 17:01:02 [INFO]\t[TRAIN] Epoch=12/100, Step=21/43, loss=17.941284, lr=6.2e-05, time_each_step=0.31s, eta=0:24:5\n",
      "2021-08-13 17:01:02 [INFO]\t[TRAIN] Epoch=12/100, Step=23/43, loss=25.9608, lr=6.2e-05, time_each_step=0.28s, eta=0:24:4\n",
      "2021-08-13 17:01:03 [INFO]\t[TRAIN] Epoch=12/100, Step=25/43, loss=12.902475, lr=6.2e-05, time_each_step=0.27s, eta=0:24:3\n",
      "2021-08-13 17:01:03 [INFO]\t[TRAIN] Epoch=12/100, Step=27/43, loss=17.695513, lr=6.2e-05, time_each_step=0.26s, eta=0:24:3\n",
      "2021-08-13 17:01:03 [INFO]\t[TRAIN] Epoch=12/100, Step=29/43, loss=12.376275, lr=6.3e-05, time_each_step=0.24s, eta=0:24:2\n",
      "2021-08-13 17:01:04 [INFO]\t[TRAIN] Epoch=12/100, Step=31/43, loss=16.656813, lr=6.3e-05, time_each_step=0.22s, eta=0:24:1\n",
      "2021-08-13 17:01:04 [INFO]\t[TRAIN] Epoch=12/100, Step=33/43, loss=11.190333, lr=6.3e-05, time_each_step=0.23s, eta=0:24:1\n",
      "2021-08-13 17:01:04 [INFO]\t[TRAIN] Epoch=12/100, Step=35/43, loss=15.245674, lr=6.3e-05, time_each_step=0.23s, eta=0:24:0\n",
      "2021-08-13 17:01:05 [INFO]\t[TRAIN] Epoch=12/100, Step=37/43, loss=15.138979, lr=6.4e-05, time_each_step=0.22s, eta=0:24:0\n",
      "2021-08-13 17:01:05 [INFO]\t[TRAIN] Epoch=12/100, Step=39/43, loss=16.085686, lr=6.4e-05, time_each_step=0.2s, eta=0:23:59\n",
      "2021-08-13 17:01:05 [INFO]\t[TRAIN] Epoch=12/100, Step=41/43, loss=17.983805, lr=6.4e-05, time_each_step=0.17s, eta=0:23:59\n",
      "2021-08-13 17:01:05 [INFO]\t[TRAIN] Epoch=12/100, Step=43/43, loss=16.248793, lr=6.4e-05, time_each_step=0.17s, eta=0:23:58\n",
      "2021-08-13 17:01:05 [INFO]\t[TRAIN] Epoch 12 finished, loss=16.868727, lr=6.2e-05 .\n",
      "2021-08-13 17:01:14 [INFO]\t[TRAIN] Epoch=13/100, Step=2/43, loss=20.888918, lr=6.5e-05, time_each_step=0.57s, eta=0:23:28\n",
      "2021-08-13 17:01:15 [INFO]\t[TRAIN] Epoch=13/100, Step=4/43, loss=16.597069, lr=6.5e-05, time_each_step=0.59s, eta=0:23:27\n",
      "2021-08-13 17:01:15 [INFO]\t[TRAIN] Epoch=13/100, Step=6/43, loss=20.209114, lr=6.5e-05, time_each_step=0.61s, eta=0:23:27\n",
      "2021-08-13 17:01:16 [INFO]\t[TRAIN] Epoch=13/100, Step=8/43, loss=9.825638, lr=6.5e-05, time_each_step=0.62s, eta=0:23:26\n",
      "2021-08-13 17:01:17 [INFO]\t[TRAIN] Epoch=13/100, Step=10/43, loss=14.943621, lr=6.6e-05, time_each_step=0.64s, eta=0:23:25\n",
      "2021-08-13 17:01:18 [INFO]\t[TRAIN] Epoch=13/100, Step=12/43, loss=18.335543, lr=6.6e-05, time_each_step=0.68s, eta=0:23:25\n",
      "2021-08-13 17:01:19 [INFO]\t[TRAIN] Epoch=13/100, Step=14/43, loss=16.014881, lr=6.6e-05, time_each_step=0.71s, eta=0:23:25\n",
      "2021-08-13 17:01:20 [INFO]\t[TRAIN] Epoch=13/100, Step=16/43, loss=18.685417, lr=6.6e-05, time_each_step=0.73s, eta=0:23:24\n",
      "2021-08-13 17:01:20 [INFO]\t[TRAIN] Epoch=13/100, Step=18/43, loss=15.517439, lr=6.7e-05, time_each_step=0.75s, eta=0:23:23\n",
      "2021-08-13 17:01:21 [INFO]\t[TRAIN] Epoch=13/100, Step=20/43, loss=12.517018, lr=6.7e-05, time_each_step=0.75s, eta=0:23:21\n",
      "2021-08-13 17:01:21 [INFO]\t[TRAIN] Epoch=13/100, Step=22/43, loss=20.106771, lr=6.7e-05, time_each_step=0.35s, eta=0:23:11\n",
      "2021-08-13 17:01:21 [INFO]\t[TRAIN] Epoch=13/100, Step=24/43, loss=21.715466, lr=6.7e-05, time_each_step=0.33s, eta=0:23:10\n",
      "2021-08-13 17:01:22 [INFO]\t[TRAIN] Epoch=13/100, Step=26/43, loss=14.257011, lr=6.8e-05, time_each_step=0.32s, eta=0:23:10\n",
      "2021-08-13 17:01:22 [INFO]\t[TRAIN] Epoch=13/100, Step=28/43, loss=13.673521, lr=6.8e-05, time_each_step=0.31s, eta=0:23:9\n",
      "2021-08-13 17:01:23 [INFO]\t[TRAIN] Epoch=13/100, Step=30/43, loss=15.303499, lr=6.8e-05, time_each_step=0.29s, eta=0:23:8\n",
      "2021-08-13 17:01:23 [INFO]\t[TRAIN] Epoch=13/100, Step=32/43, loss=15.561515, lr=6.8e-05, time_each_step=0.26s, eta=0:23:7\n",
      "2021-08-13 17:01:24 [INFO]\t[TRAIN] Epoch=13/100, Step=34/43, loss=15.869693, lr=6.9e-05, time_each_step=0.23s, eta=0:23:6\n",
      "2021-08-13 17:01:24 [INFO]\t[TRAIN] Epoch=13/100, Step=36/43, loss=17.027973, lr=6.9e-05, time_each_step=0.22s, eta=0:23:6\n",
      "2021-08-13 17:01:24 [INFO]\t[TRAIN] Epoch=13/100, Step=38/43, loss=14.290866, lr=6.9e-05, time_each_step=0.21s, eta=0:23:5\n",
      "2021-08-13 17:01:25 [INFO]\t[TRAIN] Epoch=13/100, Step=40/43, loss=17.871735, lr=6.9e-05, time_each_step=0.21s, eta=0:23:5\n",
      "2021-08-13 17:01:25 [INFO]\t[TRAIN] Epoch=13/100, Step=42/43, loss=11.418407, lr=7e-05, time_each_step=0.2s, eta=0:23:4\n",
      "2021-08-13 17:01:25 [INFO]\t[TRAIN] Epoch 13 finished, loss=16.632912, lr=6.7e-05 .\n",
      "2021-08-13 17:01:30 [INFO]\t[TRAIN] Epoch=14/100, Step=1/43, loss=17.987799, lr=7e-05, time_each_step=0.45s, eta=0:29:58\n",
      "2021-08-13 17:01:31 [INFO]\t[TRAIN] Epoch=14/100, Step=3/43, loss=17.082138, lr=7e-05, time_each_step=0.47s, eta=0:29:57\n",
      "2021-08-13 17:01:32 [INFO]\t[TRAIN] Epoch=14/100, Step=5/43, loss=21.443184, lr=7e-05, time_each_step=0.49s, eta=0:29:57\n",
      "2021-08-13 17:01:33 [INFO]\t[TRAIN] Epoch=14/100, Step=7/43, loss=14.099415, lr=7.1e-05, time_each_step=0.5s, eta=0:29:56\n",
      "2021-08-13 17:01:33 [INFO]\t[TRAIN] Epoch=14/100, Step=9/43, loss=17.270681, lr=7.1e-05, time_each_step=0.51s, eta=0:29:56\n",
      "2021-08-13 17:01:34 [INFO]\t[TRAIN] Epoch=14/100, Step=11/43, loss=19.306654, lr=7.1e-05, time_each_step=0.52s, eta=0:29:55\n",
      "2021-08-13 17:01:35 [INFO]\t[TRAIN] Epoch=14/100, Step=13/43, loss=14.708606, lr=7.1e-05, time_each_step=0.53s, eta=0:29:54\n",
      "2021-08-13 17:01:36 [INFO]\t[TRAIN] Epoch=14/100, Step=15/43, loss=12.980986, lr=7.2e-05, time_each_step=0.56s, eta=0:29:54\n",
      "2021-08-13 17:01:36 [INFO]\t[TRAIN] Epoch=14/100, Step=17/43, loss=13.71211, lr=7.2e-05, time_each_step=0.57s, eta=0:29:53\n",
      "2021-08-13 17:01:37 [INFO]\t[TRAIN] Epoch=14/100, Step=19/43, loss=12.253213, lr=7.2e-05, time_each_step=0.57s, eta=0:29:52\n",
      "2021-08-13 17:01:37 [INFO]\t[TRAIN] Epoch=14/100, Step=21/43, loss=20.503466, lr=7.2e-05, time_each_step=0.33s, eta=0:29:46\n",
      "2021-08-13 17:01:38 [INFO]\t[TRAIN] Epoch=14/100, Step=23/43, loss=15.282243, lr=7.3e-05, time_each_step=0.32s, eta=0:29:45\n",
      "2021-08-13 17:01:38 [INFO]\t[TRAIN] Epoch=14/100, Step=25/43, loss=12.299383, lr=7.3e-05, time_each_step=0.3s, eta=0:29:44\n",
      "2021-08-13 17:01:39 [INFO]\t[TRAIN] Epoch=14/100, Step=27/43, loss=15.512473, lr=7.3e-05, time_each_step=0.29s, eta=0:29:43\n",
      "2021-08-13 17:01:39 [INFO]\t[TRAIN] Epoch=14/100, Step=29/43, loss=13.581312, lr=7.3e-05, time_each_step=0.28s, eta=0:29:42\n",
      "2021-08-13 17:01:40 [INFO]\t[TRAIN] Epoch=14/100, Step=31/43, loss=17.446672, lr=7.4e-05, time_each_step=0.28s, eta=0:29:42\n",
      "2021-08-13 17:01:40 [INFO]\t[TRAIN] Epoch=14/100, Step=33/43, loss=15.370969, lr=7.4e-05, time_each_step=0.26s, eta=0:29:41\n",
      "2021-08-13 17:01:40 [INFO]\t[TRAIN] Epoch=14/100, Step=35/43, loss=16.139448, lr=7.4e-05, time_each_step=0.23s, eta=0:29:40\n",
      "2021-08-13 17:01:41 [INFO]\t[TRAIN] Epoch=14/100, Step=37/43, loss=12.024934, lr=7.4e-05, time_each_step=0.22s, eta=0:29:40\n",
      "2021-08-13 17:01:41 [INFO]\t[TRAIN] Epoch=14/100, Step=39/43, loss=14.132505, lr=7.5e-05, time_each_step=0.22s, eta=0:29:39\n",
      "2021-08-13 17:01:41 [INFO]\t[TRAIN] Epoch=14/100, Step=41/43, loss=13.376696, lr=7.5e-05, time_each_step=0.21s, eta=0:29:39\n",
      "2021-08-13 17:01:42 [INFO]\t[TRAIN] Epoch=14/100, Step=43/43, loss=15.036673, lr=7.5e-05, time_each_step=0.2s, eta=0:29:38\n",
      "2021-08-13 17:01:42 [INFO]\t[TRAIN] Epoch 14 finished, loss=15.702767, lr=7.2e-05 .\n",
      "2021-08-13 17:01:49 [INFO]\t[TRAIN] Epoch=15/100, Step=2/43, loss=14.525549, lr=7.5e-05, time_each_step=0.54s, eta=0:25:2\n",
      "2021-08-13 17:01:49 [INFO]\t[TRAIN] Epoch=15/100, Step=4/43, loss=21.232946, lr=7.6e-05, time_each_step=0.55s, eta=0:25:1\n",
      "2021-08-13 17:01:50 [INFO]\t[TRAIN] Epoch=15/100, Step=6/43, loss=22.232931, lr=7.6e-05, time_each_step=0.56s, eta=0:25:0\n",
      "2021-08-13 17:01:51 [INFO]\t[TRAIN] Epoch=15/100, Step=8/43, loss=15.820111, lr=7.6e-05, time_each_step=0.57s, eta=0:25:0\n",
      "2021-08-13 17:01:52 [INFO]\t[TRAIN] Epoch=15/100, Step=10/43, loss=18.458839, lr=7.6e-05, time_each_step=0.58s, eta=0:24:59\n",
      "2021-08-13 17:01:52 [INFO]\t[TRAIN] Epoch=15/100, Step=12/43, loss=16.552963, lr=7.7e-05, time_each_step=0.59s, eta=0:24:58\n",
      "2021-08-13 17:01:53 [INFO]\t[TRAIN] Epoch=15/100, Step=14/43, loss=16.543274, lr=7.7e-05, time_each_step=0.61s, eta=0:24:57\n",
      "2021-08-13 17:01:53 [INFO]\t[TRAIN] Epoch=15/100, Step=16/43, loss=11.462448, lr=7.7e-05, time_each_step=0.62s, eta=0:24:56\n",
      "2021-08-13 17:01:54 [INFO]\t[TRAIN] Epoch=15/100, Step=18/43, loss=14.308113, lr=7.7e-05, time_each_step=0.63s, eta=0:24:55\n",
      "2021-08-13 17:01:54 [INFO]\t[TRAIN] Epoch=15/100, Step=20/43, loss=15.056751, lr=7.8e-05, time_each_step=0.63s, eta=0:24:54\n",
      "2021-08-13 17:01:55 [INFO]\t[TRAIN] Epoch=15/100, Step=22/43, loss=16.423328, lr=7.8e-05, time_each_step=0.3s, eta=0:24:46\n",
      "2021-08-13 17:01:55 [INFO]\t[TRAIN] Epoch=15/100, Step=24/43, loss=15.027714, lr=7.8e-05, time_each_step=0.28s, eta=0:24:45\n",
      "2021-08-13 17:01:56 [INFO]\t[TRAIN] Epoch=15/100, Step=26/43, loss=17.058119, lr=7.8e-05, time_each_step=0.27s, eta=0:24:44\n",
      "2021-08-13 17:01:56 [INFO]\t[TRAIN] Epoch=15/100, Step=28/43, loss=20.391327, lr=7.9e-05, time_each_step=0.26s, eta=0:24:43\n",
      "2021-08-13 17:01:57 [INFO]\t[TRAIN] Epoch=15/100, Step=30/43, loss=20.457506, lr=7.9e-05, time_each_step=0.26s, eta=0:24:43\n",
      "2021-08-13 17:01:57 [INFO]\t[TRAIN] Epoch=15/100, Step=32/43, loss=15.682938, lr=7.9e-05, time_each_step=0.26s, eta=0:24:42\n",
      "2021-08-13 17:01:58 [INFO]\t[TRAIN] Epoch=15/100, Step=34/43, loss=13.809218, lr=7.9e-05, time_each_step=0.24s, eta=0:24:42\n",
      "2021-08-13 17:01:58 [INFO]\t[TRAIN] Epoch=15/100, Step=36/43, loss=9.051285, lr=8e-05, time_each_step=0.24s, eta=0:24:41\n",
      "2021-08-13 17:01:58 [INFO]\t[TRAIN] Epoch=15/100, Step=38/43, loss=32.437855, lr=8e-05, time_each_step=0.23s, eta=0:24:41\n",
      "2021-08-13 17:01:59 [INFO]\t[TRAIN] Epoch=15/100, Step=40/43, loss=9.772052, lr=8e-05, time_each_step=0.23s, eta=0:24:40\n",
      "2021-08-13 17:01:59 [INFO]\t[TRAIN] Epoch=15/100, Step=42/43, loss=11.825605, lr=8e-05, time_each_step=0.23s, eta=0:24:40\n",
      "2021-08-13 17:02:00 [INFO]\t[TRAIN] Epoch 15 finished, loss=15.615792, lr=7.8e-05 .\n",
      "2021-08-13 17:02:06 [INFO]\t[TRAIN] Epoch=16/100, Step=1/43, loss=13.81488, lr=8.1e-05, time_each_step=0.52s, eta=0:26:51\n",
      "2021-08-13 17:02:06 [INFO]\t[TRAIN] Epoch=16/100, Step=3/43, loss=19.496254, lr=8.1e-05, time_each_step=0.53s, eta=0:26:50\n",
      "2021-08-13 17:02:07 [INFO]\t[TRAIN] Epoch=16/100, Step=5/43, loss=12.74592, lr=8.1e-05, time_each_step=0.54s, eta=0:26:50\n",
      "2021-08-13 17:02:08 [INFO]\t[TRAIN] Epoch=16/100, Step=7/43, loss=15.439439, lr=8.1e-05, time_each_step=0.55s, eta=0:26:48\n",
      "2021-08-13 17:02:08 [INFO]\t[TRAIN] Epoch=16/100, Step=9/43, loss=11.015005, lr=8.2e-05, time_each_step=0.55s, eta=0:26:48\n",
      "2021-08-13 17:02:09 [INFO]\t[TRAIN] Epoch=16/100, Step=11/43, loss=15.274268, lr=8.2e-05, time_each_step=0.57s, eta=0:26:47\n",
      "2021-08-13 17:02:10 [INFO]\t[TRAIN] Epoch=16/100, Step=13/43, loss=13.896308, lr=8.2e-05, time_each_step=0.58s, eta=0:26:46\n",
      "2021-08-13 17:02:11 [INFO]\t[TRAIN] Epoch=16/100, Step=15/43, loss=13.577963, lr=8.2e-05, time_each_step=0.6s, eta=0:26:46\n",
      "2021-08-13 17:02:11 [INFO]\t[TRAIN] Epoch=16/100, Step=17/43, loss=11.852483, lr=8.3e-05, time_each_step=0.63s, eta=0:26:45\n",
      "2021-08-13 17:02:12 [INFO]\t[TRAIN] Epoch=16/100, Step=19/43, loss=22.755909, lr=8.3e-05, time_each_step=0.62s, eta=0:26:44\n",
      "2021-08-13 17:02:12 [INFO]\t[TRAIN] Epoch=16/100, Step=21/43, loss=10.753725, lr=8.3e-05, time_each_step=0.33s, eta=0:26:36\n",
      "2021-08-13 17:02:12 [INFO]\t[TRAIN] Epoch=16/100, Step=23/43, loss=13.086207, lr=8.3e-05, time_each_step=0.31s, eta=0:26:35\n",
      "2021-08-13 17:02:13 [INFO]\t[TRAIN] Epoch=16/100, Step=25/43, loss=17.238216, lr=8.4e-05, time_each_step=0.29s, eta=0:26:34\n",
      "2021-08-13 17:02:13 [INFO]\t[TRAIN] Epoch=16/100, Step=27/43, loss=13.183256, lr=8.4e-05, time_each_step=0.28s, eta=0:26:33\n",
      "2021-08-13 17:02:14 [INFO]\t[TRAIN] Epoch=16/100, Step=29/43, loss=12.851639, lr=8.4e-05, time_each_step=0.27s, eta=0:26:33\n",
      "2021-08-13 17:02:14 [INFO]\t[TRAIN] Epoch=16/100, Step=31/43, loss=11.535088, lr=8.4e-05, time_each_step=0.25s, eta=0:26:32\n",
      "2021-08-13 17:02:14 [INFO]\t[TRAIN] Epoch=16/100, Step=33/43, loss=17.019337, lr=8.5e-05, time_each_step=0.23s, eta=0:26:31\n",
      "2021-08-13 17:02:15 [INFO]\t[TRAIN] Epoch=16/100, Step=35/43, loss=12.169122, lr=8.5e-05, time_each_step=0.21s, eta=0:26:30\n",
      "2021-08-13 17:02:15 [INFO]\t[TRAIN] Epoch=16/100, Step=37/43, loss=15.995418, lr=8.5e-05, time_each_step=0.19s, eta=0:26:30\n",
      "2021-08-13 17:02:16 [INFO]\t[TRAIN] Epoch=16/100, Step=39/43, loss=8.650824, lr=8.5e-05, time_each_step=0.2s, eta=0:26:30\n",
      "2021-08-13 17:02:16 [INFO]\t[TRAIN] Epoch=16/100, Step=41/43, loss=16.862446, lr=8.6e-05, time_each_step=0.19s, eta=0:26:29\n",
      "2021-08-13 17:02:17 [INFO]\t[TRAIN] Epoch=16/100, Step=43/43, loss=10.503612, lr=8.6e-05, time_each_step=0.21s, eta=0:26:29\n",
      "2021-08-13 17:02:17 [INFO]\t[TRAIN] Epoch 16 finished, loss=14.239328, lr=8.3e-05 .\n",
      "2021-08-13 17:02:21 [INFO]\t[TRAIN] Epoch=17/100, Step=2/43, loss=12.742305, lr=8.6e-05, time_each_step=0.41s, eta=0:25:14\n",
      "2021-08-13 17:02:22 [INFO]\t[TRAIN] Epoch=17/100, Step=4/43, loss=16.179947, lr=8.6e-05, time_each_step=0.42s, eta=0:25:14\n",
      "2021-08-13 17:02:22 [INFO]\t[TRAIN] Epoch=17/100, Step=6/43, loss=15.611797, lr=8.7e-05, time_each_step=0.43s, eta=0:25:14\n",
      "2021-08-13 17:02:23 [INFO]\t[TRAIN] Epoch=17/100, Step=8/43, loss=12.950116, lr=8.7e-05, time_each_step=0.45s, eta=0:25:13\n",
      "2021-08-13 17:02:23 [INFO]\t[TRAIN] Epoch=17/100, Step=10/43, loss=22.177956, lr=8.7e-05, time_each_step=0.45s, eta=0:25:12\n",
      "2021-08-13 17:02:24 [INFO]\t[TRAIN] Epoch=17/100, Step=12/43, loss=13.8603, lr=8.7e-05, time_each_step=0.46s, eta=0:25:12\n",
      "2021-08-13 17:02:24 [INFO]\t[TRAIN] Epoch=17/100, Step=14/43, loss=15.074602, lr=8.8e-05, time_each_step=0.46s, eta=0:25:11\n",
      "2021-08-13 17:02:25 [INFO]\t[TRAIN] Epoch=17/100, Step=16/43, loss=11.915227, lr=8.8e-05, time_each_step=0.46s, eta=0:25:10\n",
      "2021-08-13 17:02:26 [INFO]\t[TRAIN] Epoch=17/100, Step=18/43, loss=10.646235, lr=8.8e-05, time_each_step=0.48s, eta=0:25:10\n",
      "2021-08-13 17:02:26 [INFO]\t[TRAIN] Epoch=17/100, Step=20/43, loss=19.678291, lr=8.8e-05, time_each_step=0.48s, eta=0:25:9\n",
      "2021-08-13 17:02:27 [INFO]\t[TRAIN] Epoch=17/100, Step=22/43, loss=15.584408, lr=8.9e-05, time_each_step=0.29s, eta=0:25:3\n",
      "2021-08-13 17:02:27 [INFO]\t[TRAIN] Epoch=17/100, Step=24/43, loss=11.645069, lr=8.9e-05, time_each_step=0.28s, eta=0:25:3\n",
      "2021-08-13 17:02:28 [INFO]\t[TRAIN] Epoch=17/100, Step=26/43, loss=12.716684, lr=8.9e-05, time_each_step=0.27s, eta=0:25:2\n",
      "2021-08-13 17:02:28 [INFO]\t[TRAIN] Epoch=17/100, Step=28/43, loss=11.425892, lr=8.9e-05, time_each_step=0.26s, eta=0:25:1\n",
      "2021-08-13 17:02:29 [INFO]\t[TRAIN] Epoch=17/100, Step=30/43, loss=12.815219, lr=9e-05, time_each_step=0.25s, eta=0:25:1\n",
      "2021-08-13 17:02:29 [INFO]\t[TRAIN] Epoch=17/100, Step=32/43, loss=13.763475, lr=9e-05, time_each_step=0.25s, eta=0:25:0\n",
      "2021-08-13 17:02:29 [INFO]\t[TRAIN] Epoch=17/100, Step=34/43, loss=15.060942, lr=9e-05, time_each_step=0.24s, eta=0:25:0\n",
      "2021-08-13 17:02:30 [INFO]\t[TRAIN] Epoch=17/100, Step=36/43, loss=21.10062, lr=9e-05, time_each_step=0.24s, eta=0:24:59\n",
      "2021-08-13 17:02:30 [INFO]\t[TRAIN] Epoch=17/100, Step=38/43, loss=16.088207, lr=9.1e-05, time_each_step=0.22s, eta=0:24:59\n",
      "2021-08-13 17:02:31 [INFO]\t[TRAIN] Epoch=17/100, Step=40/43, loss=13.536068, lr=9.1e-05, time_each_step=0.21s, eta=0:24:58\n",
      "2021-08-13 17:02:31 [INFO]\t[TRAIN] Epoch=17/100, Step=42/43, loss=14.771071, lr=9.1e-05, time_each_step=0.21s, eta=0:24:58\n",
      "2021-08-13 17:02:31 [INFO]\t[TRAIN] Epoch 17 finished, loss=14.729542, lr=8.9e-05 .\n",
      "2021-08-13 17:02:36 [INFO]\t[TRAIN] Epoch=18/100, Step=1/43, loss=14.902378, lr=9.1e-05, time_each_step=0.43s, eta=0:21:32\n",
      "2021-08-13 17:02:37 [INFO]\t[TRAIN] Epoch=18/100, Step=3/43, loss=11.905756, lr=9.2e-05, time_each_step=0.44s, eta=0:21:31\n",
      "2021-08-13 17:02:37 [INFO]\t[TRAIN] Epoch=18/100, Step=5/43, loss=18.646763, lr=9.2e-05, time_each_step=0.45s, eta=0:21:31\n",
      "2021-08-13 17:02:38 [INFO]\t[TRAIN] Epoch=18/100, Step=7/43, loss=12.344544, lr=9.2e-05, time_each_step=0.46s, eta=0:21:30\n",
      "2021-08-13 17:02:38 [INFO]\t[TRAIN] Epoch=18/100, Step=9/43, loss=13.145089, lr=9.2e-05, time_each_step=0.46s, eta=0:21:29\n",
      "2021-08-13 17:02:39 [INFO]\t[TRAIN] Epoch=18/100, Step=11/43, loss=15.331085, lr=9.3e-05, time_each_step=0.48s, eta=0:21:29\n",
      "2021-08-13 17:02:39 [INFO]\t[TRAIN] Epoch=18/100, Step=13/43, loss=19.641644, lr=9.3e-05, time_each_step=0.47s, eta=0:21:28\n",
      "2021-08-13 17:02:40 [INFO]\t[TRAIN] Epoch=18/100, Step=15/43, loss=10.248693, lr=9.3e-05, time_each_step=0.49s, eta=0:21:27\n",
      "2021-08-13 17:02:40 [INFO]\t[TRAIN] Epoch=18/100, Step=17/43, loss=9.680105, lr=9.3e-05, time_each_step=0.48s, eta=0:21:26\n",
      "2021-08-13 17:02:41 [INFO]\t[TRAIN] Epoch=18/100, Step=19/43, loss=12.011497, lr=9.4e-05, time_each_step=0.5s, eta=0:21:26\n",
      "2021-08-13 17:02:41 [INFO]\t[TRAIN] Epoch=18/100, Step=21/43, loss=11.465185, lr=9.4e-05, time_each_step=0.28s, eta=0:21:20\n",
      "2021-08-13 17:02:42 [INFO]\t[TRAIN] Epoch=18/100, Step=23/43, loss=13.656254, lr=9.4e-05, time_each_step=0.27s, eta=0:21:19\n",
      "2021-08-13 17:02:42 [INFO]\t[TRAIN] Epoch=18/100, Step=25/43, loss=24.517704, lr=9.4e-05, time_each_step=0.25s, eta=0:21:18\n",
      "2021-08-13 17:02:43 [INFO]\t[TRAIN] Epoch=18/100, Step=27/43, loss=12.040788, lr=9.5e-05, time_each_step=0.26s, eta=0:21:18\n",
      "2021-08-13 17:02:43 [INFO]\t[TRAIN] Epoch=18/100, Step=29/43, loss=11.644577, lr=9.5e-05, time_each_step=0.25s, eta=0:21:17\n",
      "2021-08-13 17:02:43 [INFO]\t[TRAIN] Epoch=18/100, Step=31/43, loss=10.498927, lr=9.5e-05, time_each_step=0.23s, eta=0:21:16\n",
      "2021-08-13 17:02:44 [INFO]\t[TRAIN] Epoch=18/100, Step=33/43, loss=12.40584, lr=9.5e-05, time_each_step=0.23s, eta=0:21:16\n",
      "2021-08-13 17:02:44 [INFO]\t[TRAIN] Epoch=18/100, Step=35/43, loss=13.070906, lr=9.6e-05, time_each_step=0.22s, eta=0:21:15\n",
      "2021-08-13 17:02:45 [INFO]\t[TRAIN] Epoch=18/100, Step=37/43, loss=16.064234, lr=9.6e-05, time_each_step=0.23s, eta=0:21:15\n",
      "2021-08-13 17:02:45 [INFO]\t[TRAIN] Epoch=18/100, Step=39/43, loss=16.060135, lr=9.6e-05, time_each_step=0.21s, eta=0:21:14\n",
      "2021-08-13 17:02:46 [INFO]\t[TRAIN] Epoch=18/100, Step=41/43, loss=11.124357, lr=9.6e-05, time_each_step=0.21s, eta=0:21:14\n",
      "2021-08-13 17:02:46 [INFO]\t[TRAIN] Epoch=18/100, Step=43/43, loss=18.271603, lr=9.7e-05, time_each_step=0.21s, eta=0:21:14\n",
      "2021-08-13 17:02:46 [INFO]\t[TRAIN] Epoch 18 finished, loss=14.406867, lr=9.4e-05 .\n",
      "2021-08-13 17:02:50 [INFO]\t[TRAIN] Epoch=19/100, Step=2/43, loss=17.054499, lr=9.7e-05, time_each_step=0.39s, eta=0:21:46\n",
      "2021-08-13 17:02:51 [INFO]\t[TRAIN] Epoch=19/100, Step=4/43, loss=10.05485, lr=9.7e-05, time_each_step=0.4s, eta=0:21:46\n",
      "2021-08-13 17:02:52 [INFO]\t[TRAIN] Epoch=19/100, Step=6/43, loss=18.512833, lr=9.7e-05, time_each_step=0.42s, eta=0:21:46\n",
      "2021-08-13 17:02:52 [INFO]\t[TRAIN] Epoch=19/100, Step=8/43, loss=12.235519, lr=9.8e-05, time_each_step=0.44s, eta=0:21:45\n",
      "2021-08-13 17:02:53 [INFO]\t[TRAIN] Epoch=19/100, Step=10/43, loss=15.05044, lr=9.8e-05, time_each_step=0.45s, eta=0:21:45\n",
      "2021-08-13 17:02:53 [INFO]\t[TRAIN] Epoch=19/100, Step=12/43, loss=14.167244, lr=9.8e-05, time_each_step=0.46s, eta=0:21:44\n",
      "2021-08-13 17:02:54 [INFO]\t[TRAIN] Epoch=19/100, Step=14/43, loss=9.855282, lr=9.8e-05, time_each_step=0.48s, eta=0:21:44\n",
      "2021-08-13 17:02:55 [INFO]\t[TRAIN] Epoch=19/100, Step=16/43, loss=15.627218, lr=9.9e-05, time_each_step=0.48s, eta=0:21:43\n",
      "2021-08-13 17:02:55 [INFO]\t[TRAIN] Epoch=19/100, Step=18/43, loss=19.683376, lr=9.9e-05, time_each_step=0.48s, eta=0:21:42\n",
      "2021-08-13 17:02:56 [INFO]\t[TRAIN] Epoch=19/100, Step=20/43, loss=13.663835, lr=9.9e-05, time_each_step=0.49s, eta=0:21:41\n",
      "2021-08-13 17:02:57 [INFO]\t[TRAIN] Epoch=19/100, Step=22/43, loss=13.547452, lr=9.9e-05, time_each_step=0.32s, eta=0:21:37\n",
      "2021-08-13 17:02:57 [INFO]\t[TRAIN] Epoch=19/100, Step=24/43, loss=16.680296, lr=0.0001, time_each_step=0.3s, eta=0:21:36\n",
      "2021-08-13 17:02:57 [INFO]\t[TRAIN] Epoch=19/100, Step=26/43, loss=11.361627, lr=0.0001, time_each_step=0.28s, eta=0:21:35\n",
      "2021-08-13 17:02:58 [INFO]\t[TRAIN] Epoch=19/100, Step=28/43, loss=16.386875, lr=0.0001, time_each_step=0.27s, eta=0:21:34\n",
      "2021-08-13 17:02:58 [INFO]\t[TRAIN] Epoch=19/100, Step=30/43, loss=12.702002, lr=0.0001, time_each_step=0.26s, eta=0:21:33\n",
      "2021-08-13 17:02:58 [INFO]\t[TRAIN] Epoch=19/100, Step=32/43, loss=16.281404, lr=0.000101, time_each_step=0.24s, eta=0:21:33\n",
      "2021-08-13 17:02:59 [INFO]\t[TRAIN] Epoch=19/100, Step=34/43, loss=15.255247, lr=0.000101, time_each_step=0.22s, eta=0:21:32\n",
      "2021-08-13 17:02:59 [INFO]\t[TRAIN] Epoch=19/100, Step=36/43, loss=16.217613, lr=0.000101, time_each_step=0.21s, eta=0:21:31\n",
      "2021-08-13 17:02:59 [INFO]\t[TRAIN] Epoch=19/100, Step=38/43, loss=9.005708, lr=0.000101, time_each_step=0.21s, eta=0:21:31\n",
      "2021-08-13 17:03:00 [INFO]\t[TRAIN] Epoch=19/100, Step=40/43, loss=9.584972, lr=0.000102, time_each_step=0.19s, eta=0:21:31\n",
      "2021-08-13 17:03:00 [INFO]\t[TRAIN] Epoch=19/100, Step=42/43, loss=10.02924, lr=0.000102, time_each_step=0.18s, eta=0:21:30\n",
      "2021-08-13 17:03:00 [INFO]\t[TRAIN] Epoch 19 finished, loss=14.378067, lr=9.9e-05 .\n",
      "2021-08-13 17:03:09 [INFO]\t[TRAIN] Epoch=20/100, Step=1/43, loss=10.840482, lr=0.000102, time_each_step=0.63s, eta=0:21:0\n",
      "2021-08-13 17:03:10 [INFO]\t[TRAIN] Epoch=20/100, Step=3/43, loss=12.035561, lr=0.000102, time_each_step=0.65s, eta=0:21:0\n",
      "2021-08-13 17:03:11 [INFO]\t[TRAIN] Epoch=20/100, Step=5/43, loss=14.482317, lr=0.000103, time_each_step=0.66s, eta=0:20:59\n",
      "2021-08-13 17:03:11 [INFO]\t[TRAIN] Epoch=20/100, Step=7/43, loss=14.076576, lr=0.000103, time_each_step=0.65s, eta=0:20:57\n",
      "2021-08-13 17:03:12 [INFO]\t[TRAIN] Epoch=20/100, Step=9/43, loss=10.396729, lr=0.000103, time_each_step=0.67s, eta=0:20:57\n",
      "2021-08-13 17:03:12 [INFO]\t[TRAIN] Epoch=20/100, Step=11/43, loss=14.185527, lr=0.000103, time_each_step=0.69s, eta=0:20:56\n",
      "2021-08-13 17:03:13 [INFO]\t[TRAIN] Epoch=20/100, Step=13/43, loss=12.581684, lr=0.000104, time_each_step=0.7s, eta=0:20:55\n",
      "2021-08-13 17:03:14 [INFO]\t[TRAIN] Epoch=20/100, Step=15/43, loss=11.085217, lr=0.000104, time_each_step=0.71s, eta=0:20:54\n",
      "2021-08-13 17:03:15 [INFO]\t[TRAIN] Epoch=20/100, Step=17/43, loss=15.894118, lr=0.000104, time_each_step=0.76s, eta=0:20:53\n",
      "2021-08-13 17:03:15 [INFO]\t[TRAIN] Epoch=20/100, Step=19/43, loss=13.951498, lr=0.000104, time_each_step=0.76s, eta=0:20:52\n",
      "2021-08-13 17:03:16 [INFO]\t[TRAIN] Epoch=20/100, Step=21/43, loss=19.501537, lr=0.000105, time_each_step=0.32s, eta=0:20:41\n",
      "2021-08-13 17:03:16 [INFO]\t[TRAIN] Epoch=20/100, Step=23/43, loss=13.316788, lr=0.000105, time_each_step=0.31s, eta=0:20:40\n",
      "2021-08-13 17:03:17 [INFO]\t[TRAIN] Epoch=20/100, Step=25/43, loss=12.917522, lr=0.000105, time_each_step=0.32s, eta=0:20:39\n",
      "2021-08-13 17:03:17 [INFO]\t[TRAIN] Epoch=20/100, Step=27/43, loss=10.879766, lr=0.000105, time_each_step=0.31s, eta=0:20:39\n",
      "2021-08-13 17:03:18 [INFO]\t[TRAIN] Epoch=20/100, Step=29/43, loss=18.735079, lr=0.000106, time_each_step=0.31s, eta=0:20:38\n",
      "2021-08-13 17:03:18 [INFO]\t[TRAIN] Epoch=20/100, Step=31/43, loss=12.227829, lr=0.000106, time_each_step=0.29s, eta=0:20:37\n",
      "2021-08-13 17:03:19 [INFO]\t[TRAIN] Epoch=20/100, Step=33/43, loss=10.313991, lr=0.000106, time_each_step=0.28s, eta=0:20:37\n",
      "2021-08-13 17:03:19 [INFO]\t[TRAIN] Epoch=20/100, Step=35/43, loss=11.740396, lr=0.000106, time_each_step=0.26s, eta=0:20:36\n",
      "2021-08-13 17:03:19 [INFO]\t[TRAIN] Epoch=20/100, Step=37/43, loss=11.530975, lr=0.000107, time_each_step=0.22s, eta=0:20:35\n",
      "2021-08-13 17:03:20 [INFO]\t[TRAIN] Epoch=20/100, Step=39/43, loss=22.86871, lr=0.000107, time_each_step=0.22s, eta=0:20:35\n",
      "2021-08-13 17:03:20 [INFO]\t[TRAIN] Epoch=20/100, Step=41/43, loss=10.832545, lr=0.000107, time_each_step=0.2s, eta=0:20:34\n",
      "2021-08-13 17:03:20 [INFO]\t[TRAIN] Epoch=20/100, Step=43/43, loss=14.870014, lr=0.000107, time_each_step=0.2s, eta=0:20:34\n",
      "2021-08-13 17:03:20 [INFO]\t[TRAIN] Epoch 20 finished, loss=13.802374, lr=0.000105 .\n",
      "2021-08-13 17:03:20 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:07<00:00,  1.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:03:28 [INFO]\t[EVAL] Finished, Epoch=20, bbox_map=17.448387 .\n",
      "2021-08-13 17:03:30 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:03:31 [INFO]\tModel saved in output/yolov3_darknet53/epoch_20.\n",
      "2021-08-13 17:03:31 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_20, bbox_map=17.44838675594424\n",
      "2021-08-13 17:03:36 [INFO]\t[TRAIN] Epoch=21/100, Step=2/43, loss=7.781749, lr=0.000108, time_each_step=0.45s, eta=0:27:51\n",
      "2021-08-13 17:03:37 [INFO]\t[TRAIN] Epoch=21/100, Step=4/43, loss=12.833333, lr=0.000108, time_each_step=0.46s, eta=0:27:50\n",
      "2021-08-13 17:03:38 [INFO]\t[TRAIN] Epoch=21/100, Step=6/43, loss=23.511032, lr=0.000108, time_each_step=0.47s, eta=0:27:50\n",
      "2021-08-13 17:03:38 [INFO]\t[TRAIN] Epoch=21/100, Step=8/43, loss=10.236883, lr=0.000108, time_each_step=0.46s, eta=0:27:49\n",
      "2021-08-13 17:03:38 [INFO]\t[TRAIN] Epoch=21/100, Step=10/43, loss=12.712276, lr=0.000109, time_each_step=0.47s, eta=0:27:48\n",
      "2021-08-13 17:03:39 [INFO]\t[TRAIN] Epoch=21/100, Step=12/43, loss=10.476775, lr=0.000109, time_each_step=0.5s, eta=0:27:48\n",
      "2021-08-13 17:03:40 [INFO]\t[TRAIN] Epoch=21/100, Step=14/43, loss=16.585979, lr=0.000109, time_each_step=0.5s, eta=0:27:47\n",
      "2021-08-13 17:03:40 [INFO]\t[TRAIN] Epoch=21/100, Step=16/43, loss=14.359654, lr=0.000109, time_each_step=0.51s, eta=0:27:46\n",
      "2021-08-13 17:03:41 [INFO]\t[TRAIN] Epoch=21/100, Step=18/43, loss=12.20042, lr=0.00011, time_each_step=0.52s, eta=0:27:45\n",
      "2021-08-13 17:03:41 [INFO]\t[TRAIN] Epoch=21/100, Step=20/43, loss=18.804581, lr=0.00011, time_each_step=0.52s, eta=0:27:44\n",
      "2021-08-13 17:03:42 [INFO]\t[TRAIN] Epoch=21/100, Step=22/43, loss=16.311317, lr=0.00011, time_each_step=0.28s, eta=0:27:38\n",
      "2021-08-13 17:03:42 [INFO]\t[TRAIN] Epoch=21/100, Step=24/43, loss=11.822364, lr=0.00011, time_each_step=0.28s, eta=0:27:38\n",
      "2021-08-13 17:03:43 [INFO]\t[TRAIN] Epoch=21/100, Step=26/43, loss=9.129491, lr=0.000111, time_each_step=0.26s, eta=0:27:37\n",
      "2021-08-13 17:03:43 [INFO]\t[TRAIN] Epoch=21/100, Step=28/43, loss=17.831753, lr=0.000111, time_each_step=0.26s, eta=0:27:36\n",
      "2021-08-13 17:03:43 [INFO]\t[TRAIN] Epoch=21/100, Step=30/43, loss=10.130095, lr=0.000111, time_each_step=0.25s, eta=0:27:36\n",
      "2021-08-13 17:03:44 [INFO]\t[TRAIN] Epoch=21/100, Step=32/43, loss=22.003599, lr=0.000111, time_each_step=0.23s, eta=0:27:35\n",
      "2021-08-13 17:03:44 [INFO]\t[TRAIN] Epoch=21/100, Step=34/43, loss=12.1112, lr=0.000112, time_each_step=0.23s, eta=0:27:34\n",
      "2021-08-13 17:03:44 [INFO]\t[TRAIN] Epoch=21/100, Step=36/43, loss=12.918473, lr=0.000112, time_each_step=0.22s, eta=0:27:34\n",
      "2021-08-13 17:03:45 [INFO]\t[TRAIN] Epoch=21/100, Step=38/43, loss=10.763592, lr=0.000112, time_each_step=0.21s, eta=0:27:33\n",
      "2021-08-13 17:03:45 [INFO]\t[TRAIN] Epoch=21/100, Step=40/43, loss=12.068399, lr=0.000112, time_each_step=0.2s, eta=0:27:33\n",
      "2021-08-13 17:03:46 [INFO]\t[TRAIN] Epoch=21/100, Step=42/43, loss=12.030464, lr=0.000113, time_each_step=0.19s, eta=0:27:33\n",
      "2021-08-13 17:03:46 [INFO]\t[TRAIN] Epoch 21 finished, loss=13.692379, lr=0.00011 .\n",
      "2021-08-13 17:03:50 [INFO]\t[TRAIN] Epoch=22/100, Step=1/43, loss=11.071234, lr=0.000113, time_each_step=0.36s, eta=0:21:19\n",
      "2021-08-13 17:03:50 [INFO]\t[TRAIN] Epoch=22/100, Step=3/43, loss=9.347637, lr=0.000113, time_each_step=0.37s, eta=0:21:19\n",
      "2021-08-13 17:03:51 [INFO]\t[TRAIN] Epoch=22/100, Step=5/43, loss=11.990243, lr=0.000113, time_each_step=0.38s, eta=0:21:18\n",
      "2021-08-13 17:03:51 [INFO]\t[TRAIN] Epoch=22/100, Step=7/43, loss=16.695858, lr=0.000114, time_each_step=0.4s, eta=0:21:18\n",
      "2021-08-13 17:03:52 [INFO]\t[TRAIN] Epoch=22/100, Step=9/43, loss=12.866348, lr=0.000114, time_each_step=0.42s, eta=0:21:18\n",
      "2021-08-13 17:03:53 [INFO]\t[TRAIN] Epoch=22/100, Step=11/43, loss=11.906942, lr=0.000114, time_each_step=0.44s, eta=0:21:18\n",
      "2021-08-13 17:03:54 [INFO]\t[TRAIN] Epoch=22/100, Step=13/43, loss=13.262545, lr=0.000114, time_each_step=0.46s, eta=0:21:17\n",
      "2021-08-13 17:03:54 [INFO]\t[TRAIN] Epoch=22/100, Step=15/43, loss=10.803822, lr=0.000115, time_each_step=0.47s, eta=0:21:17\n",
      "2021-08-13 17:03:55 [INFO]\t[TRAIN] Epoch=22/100, Step=17/43, loss=11.368324, lr=0.000115, time_each_step=0.47s, eta=0:21:16\n",
      "2021-08-13 17:03:55 [INFO]\t[TRAIN] Epoch=22/100, Step=19/43, loss=10.068578, lr=0.000115, time_each_step=0.47s, eta=0:21:15\n",
      "2021-08-13 17:03:55 [INFO]\t[TRAIN] Epoch=22/100, Step=21/43, loss=14.796, lr=0.000115, time_each_step=0.29s, eta=0:21:10\n",
      "2021-08-13 17:03:56 [INFO]\t[TRAIN] Epoch=22/100, Step=23/43, loss=21.562565, lr=0.000116, time_each_step=0.28s, eta=0:21:9\n",
      "2021-08-13 17:03:56 [INFO]\t[TRAIN] Epoch=22/100, Step=25/43, loss=9.653209, lr=0.000116, time_each_step=0.28s, eta=0:21:9\n",
      "2021-08-13 17:03:57 [INFO]\t[TRAIN] Epoch=22/100, Step=27/43, loss=14.117388, lr=0.000116, time_each_step=0.26s, eta=0:21:8\n",
      "2021-08-13 17:03:57 [INFO]\t[TRAIN] Epoch=22/100, Step=29/43, loss=17.158409, lr=0.000116, time_each_step=0.26s, eta=0:21:7\n",
      "2021-08-13 17:03:58 [INFO]\t[TRAIN] Epoch=22/100, Step=31/43, loss=12.543001, lr=0.000117, time_each_step=0.24s, eta=0:21:7\n",
      "2021-08-13 17:03:58 [INFO]\t[TRAIN] Epoch=22/100, Step=33/43, loss=11.386718, lr=0.000117, time_each_step=0.23s, eta=0:21:6\n",
      "2021-08-13 17:03:58 [INFO]\t[TRAIN] Epoch=22/100, Step=35/43, loss=9.67448, lr=0.000117, time_each_step=0.21s, eta=0:21:5\n",
      "2021-08-13 17:03:59 [INFO]\t[TRAIN] Epoch=22/100, Step=37/43, loss=9.964479, lr=0.000117, time_each_step=0.21s, eta=0:21:5\n",
      "2021-08-13 17:03:59 [INFO]\t[TRAIN] Epoch=22/100, Step=39/43, loss=12.649327, lr=0.000118, time_each_step=0.2s, eta=0:21:5\n",
      "2021-08-13 17:03:59 [INFO]\t[TRAIN] Epoch=22/100, Step=41/43, loss=11.373624, lr=0.000118, time_each_step=0.2s, eta=0:21:4\n",
      "2021-08-13 17:04:00 [INFO]\t[TRAIN] Epoch=22/100, Step=43/43, loss=12.317274, lr=0.000118, time_each_step=0.2s, eta=0:21:4\n",
      "2021-08-13 17:04:00 [INFO]\t[TRAIN] Epoch 22 finished, loss=12.798244, lr=0.000116 .\n",
      "2021-08-13 17:04:05 [INFO]\t[TRAIN] Epoch=23/100, Step=2/43, loss=9.454565, lr=0.000118, time_each_step=0.44s, eta=0:19:37\n",
      "2021-08-13 17:04:06 [INFO]\t[TRAIN] Epoch=23/100, Step=4/43, loss=9.674844, lr=0.000119, time_each_step=0.45s, eta=0:19:37\n",
      "2021-08-13 17:04:06 [INFO]\t[TRAIN] Epoch=23/100, Step=6/43, loss=17.545486, lr=0.000119, time_each_step=0.46s, eta=0:19:36\n",
      "2021-08-13 17:04:07 [INFO]\t[TRAIN] Epoch=23/100, Step=8/43, loss=14.152762, lr=0.000119, time_each_step=0.48s, eta=0:19:36\n",
      "2021-08-13 17:04:08 [INFO]\t[TRAIN] Epoch=23/100, Step=10/43, loss=12.740665, lr=0.000119, time_each_step=0.49s, eta=0:19:35\n",
      "2021-08-13 17:04:08 [INFO]\t[TRAIN] Epoch=23/100, Step=12/43, loss=11.554031, lr=0.00012, time_each_step=0.5s, eta=0:19:35\n",
      "2021-08-13 17:04:09 [INFO]\t[TRAIN] Epoch=23/100, Step=14/43, loss=17.047598, lr=0.00012, time_each_step=0.51s, eta=0:19:34\n",
      "2021-08-13 17:04:10 [INFO]\t[TRAIN] Epoch=23/100, Step=16/43, loss=9.277605, lr=0.00012, time_each_step=0.54s, eta=0:19:33\n",
      "2021-08-13 17:04:11 [INFO]\t[TRAIN] Epoch=23/100, Step=18/43, loss=13.852367, lr=0.00012, time_each_step=0.56s, eta=0:19:33\n",
      "2021-08-13 17:04:11 [INFO]\t[TRAIN] Epoch=23/100, Step=20/43, loss=10.512982, lr=0.000121, time_each_step=0.58s, eta=0:19:32\n",
      "2021-08-13 17:04:12 [INFO]\t[TRAIN] Epoch=23/100, Step=22/43, loss=11.154662, lr=0.000121, time_each_step=0.33s, eta=0:19:26\n",
      "2021-08-13 17:04:12 [INFO]\t[TRAIN] Epoch=23/100, Step=24/43, loss=13.646799, lr=0.000121, time_each_step=0.32s, eta=0:19:25\n",
      "2021-08-13 17:04:13 [INFO]\t[TRAIN] Epoch=23/100, Step=26/43, loss=9.872709, lr=0.000121, time_each_step=0.31s, eta=0:19:24\n",
      "2021-08-13 17:04:13 [INFO]\t[TRAIN] Epoch=23/100, Step=28/43, loss=13.263052, lr=0.000122, time_each_step=0.3s, eta=0:19:24\n",
      "2021-08-13 17:04:14 [INFO]\t[TRAIN] Epoch=23/100, Step=30/43, loss=18.013309, lr=0.000122, time_each_step=0.28s, eta=0:19:23\n",
      "2021-08-13 17:04:14 [INFO]\t[TRAIN] Epoch=23/100, Step=32/43, loss=19.116434, lr=0.000122, time_each_step=0.28s, eta=0:19:22\n",
      "2021-08-13 17:04:15 [INFO]\t[TRAIN] Epoch=23/100, Step=34/43, loss=13.004957, lr=0.000122, time_each_step=0.27s, eta=0:19:21\n",
      "2021-08-13 17:04:15 [INFO]\t[TRAIN] Epoch=23/100, Step=36/43, loss=11.132624, lr=0.000123, time_each_step=0.27s, eta=0:19:21\n",
      "2021-08-13 17:04:16 [INFO]\t[TRAIN] Epoch=23/100, Step=38/43, loss=13.668835, lr=0.000123, time_each_step=0.25s, eta=0:19:20\n",
      "2021-08-13 17:04:16 [INFO]\t[TRAIN] Epoch=23/100, Step=40/43, loss=14.826035, lr=0.000123, time_each_step=0.23s, eta=0:19:20\n",
      "2021-08-13 17:04:16 [INFO]\t[TRAIN] Epoch=23/100, Step=42/43, loss=14.190332, lr=0.000123, time_each_step=0.24s, eta=0:19:19\n",
      "2021-08-13 17:04:17 [INFO]\t[TRAIN] Epoch 23 finished, loss=13.243545, lr=0.000121 .\n",
      "2021-08-13 17:04:21 [INFO]\t[TRAIN] Epoch=24/100, Step=1/43, loss=10.227253, lr=0.000124, time_each_step=0.46s, eta=0:22:58\n",
      "2021-08-13 17:04:22 [INFO]\t[TRAIN] Epoch=24/100, Step=3/43, loss=18.947653, lr=0.000124, time_each_step=0.46s, eta=0:22:57\n",
      "2021-08-13 17:04:23 [INFO]\t[TRAIN] Epoch=24/100, Step=5/43, loss=11.963588, lr=0.000124, time_each_step=0.47s, eta=0:22:56\n",
      "2021-08-13 17:04:23 [INFO]\t[TRAIN] Epoch=24/100, Step=7/43, loss=10.627309, lr=0.000124, time_each_step=0.46s, eta=0:22:56\n",
      "2021-08-13 17:04:24 [INFO]\t[TRAIN] Epoch=24/100, Step=9/43, loss=21.587456, lr=0.000125, time_each_step=0.48s, eta=0:22:55\n",
      "2021-08-13 17:04:25 [INFO]\t[TRAIN] Epoch=24/100, Step=11/43, loss=12.192985, lr=0.000125, time_each_step=0.5s, eta=0:22:55\n",
      "2021-08-13 17:04:25 [INFO]\t[TRAIN] Epoch=24/100, Step=13/43, loss=13.580212, lr=0.000125, time_each_step=0.5s, eta=0:22:54\n",
      "2021-08-13 17:04:26 [INFO]\t[TRAIN] Epoch=24/100, Step=15/43, loss=10.249009, lr=0.000125, time_each_step=0.51s, eta=0:22:53\n",
      "2021-08-13 17:04:26 [INFO]\t[TRAIN] Epoch=24/100, Step=17/43, loss=10.178095, lr=0.000125, time_each_step=0.52s, eta=0:22:52\n",
      "2021-08-13 17:04:27 [INFO]\t[TRAIN] Epoch=24/100, Step=19/43, loss=16.337395, lr=0.000125, time_each_step=0.53s, eta=0:22:52\n",
      "2021-08-13 17:04:28 [INFO]\t[TRAIN] Epoch=24/100, Step=21/43, loss=16.594152, lr=0.000125, time_each_step=0.31s, eta=0:22:46\n",
      "2021-08-13 17:04:28 [INFO]\t[TRAIN] Epoch=24/100, Step=23/43, loss=14.779626, lr=0.000125, time_each_step=0.32s, eta=0:22:45\n",
      "2021-08-13 17:04:29 [INFO]\t[TRAIN] Epoch=24/100, Step=25/43, loss=11.994352, lr=0.000125, time_each_step=0.3s, eta=0:22:44\n",
      "2021-08-13 17:04:29 [INFO]\t[TRAIN] Epoch=24/100, Step=27/43, loss=15.80941, lr=0.000125, time_each_step=0.31s, eta=0:22:44\n",
      "2021-08-13 17:04:29 [INFO]\t[TRAIN] Epoch=24/100, Step=29/43, loss=17.645428, lr=0.000125, time_each_step=0.28s, eta=0:22:43\n",
      "2021-08-13 17:04:30 [INFO]\t[TRAIN] Epoch=24/100, Step=31/43, loss=10.673916, lr=0.000125, time_each_step=0.26s, eta=0:22:42\n",
      "2021-08-13 17:04:30 [INFO]\t[TRAIN] Epoch=24/100, Step=33/43, loss=15.057361, lr=0.000125, time_each_step=0.24s, eta=0:22:41\n",
      "2021-08-13 17:04:30 [INFO]\t[TRAIN] Epoch=24/100, Step=35/43, loss=10.25133, lr=0.000125, time_each_step=0.23s, eta=0:22:41\n",
      "2021-08-13 17:04:31 [INFO]\t[TRAIN] Epoch=24/100, Step=37/43, loss=12.426479, lr=0.000125, time_each_step=0.22s, eta=0:22:40\n",
      "2021-08-13 17:04:31 [INFO]\t[TRAIN] Epoch=24/100, Step=39/43, loss=10.42779, lr=0.000125, time_each_step=0.21s, eta=0:22:40\n",
      "2021-08-13 17:04:32 [INFO]\t[TRAIN] Epoch=24/100, Step=41/43, loss=10.773299, lr=0.000125, time_each_step=0.2s, eta=0:22:39\n",
      "2021-08-13 17:04:32 [INFO]\t[TRAIN] Epoch=24/100, Step=43/43, loss=15.721451, lr=0.000125, time_each_step=0.2s, eta=0:22:39\n",
      "2021-08-13 17:04:32 [INFO]\t[TRAIN] Epoch 24 finished, loss=13.747885, lr=0.000125 .\n",
      "2021-08-13 17:04:37 [INFO]\t[TRAIN] Epoch=25/100, Step=2/43, loss=15.185282, lr=0.000125, time_each_step=0.43s, eta=0:21:0\n",
      "2021-08-13 17:04:38 [INFO]\t[TRAIN] Epoch=25/100, Step=4/43, loss=18.890017, lr=0.000125, time_each_step=0.44s, eta=0:21:0\n",
      "2021-08-13 17:04:39 [INFO]\t[TRAIN] Epoch=25/100, Step=6/43, loss=23.126835, lr=0.000125, time_each_step=0.46s, eta=0:20:59\n",
      "2021-08-13 17:04:39 [INFO]\t[TRAIN] Epoch=25/100, Step=8/43, loss=11.087638, lr=0.000125, time_each_step=0.48s, eta=0:20:59\n",
      "2021-08-13 17:04:40 [INFO]\t[TRAIN] Epoch=25/100, Step=10/43, loss=13.352518, lr=0.000125, time_each_step=0.49s, eta=0:20:59\n",
      "2021-08-13 17:04:41 [INFO]\t[TRAIN] Epoch=25/100, Step=12/43, loss=15.739714, lr=0.000125, time_each_step=0.51s, eta=0:20:58\n",
      "2021-08-13 17:04:41 [INFO]\t[TRAIN] Epoch=25/100, Step=14/43, loss=11.175966, lr=0.000125, time_each_step=0.52s, eta=0:20:58\n",
      "2021-08-13 17:04:42 [INFO]\t[TRAIN] Epoch=25/100, Step=16/43, loss=11.276871, lr=0.000125, time_each_step=0.53s, eta=0:20:57\n",
      "2021-08-13 17:04:42 [INFO]\t[TRAIN] Epoch=25/100, Step=18/43, loss=13.830336, lr=0.000125, time_each_step=0.54s, eta=0:20:56\n",
      "2021-08-13 17:04:43 [INFO]\t[TRAIN] Epoch=25/100, Step=20/43, loss=10.117102, lr=0.000125, time_each_step=0.54s, eta=0:20:55\n",
      "2021-08-13 17:04:43 [INFO]\t[TRAIN] Epoch=25/100, Step=22/43, loss=9.818964, lr=0.000125, time_each_step=0.31s, eta=0:20:49\n",
      "2021-08-13 17:04:44 [INFO]\t[TRAIN] Epoch=25/100, Step=24/43, loss=13.177614, lr=0.000125, time_each_step=0.31s, eta=0:20:48\n",
      "2021-08-13 17:04:44 [INFO]\t[TRAIN] Epoch=25/100, Step=26/43, loss=12.619294, lr=0.000125, time_each_step=0.28s, eta=0:20:47\n",
      "2021-08-13 17:04:45 [INFO]\t[TRAIN] Epoch=25/100, Step=28/43, loss=15.584576, lr=0.000125, time_each_step=0.27s, eta=0:20:47\n",
      "2021-08-13 17:04:45 [INFO]\t[TRAIN] Epoch=25/100, Step=30/43, loss=13.140863, lr=0.000125, time_each_step=0.28s, eta=0:20:46\n",
      "2021-08-13 17:04:46 [INFO]\t[TRAIN] Epoch=25/100, Step=32/43, loss=12.421295, lr=0.000125, time_each_step=0.25s, eta=0:20:45\n",
      "2021-08-13 17:04:46 [INFO]\t[TRAIN] Epoch=25/100, Step=34/43, loss=11.113037, lr=0.000125, time_each_step=0.25s, eta=0:20:45\n",
      "2021-08-13 17:04:47 [INFO]\t[TRAIN] Epoch=25/100, Step=36/43, loss=15.630114, lr=0.000125, time_each_step=0.24s, eta=0:20:44\n",
      "2021-08-13 17:04:47 [INFO]\t[TRAIN] Epoch=25/100, Step=38/43, loss=16.857058, lr=0.000125, time_each_step=0.23s, eta=0:20:44\n",
      "2021-08-13 17:04:47 [INFO]\t[TRAIN] Epoch=25/100, Step=40/43, loss=11.782341, lr=0.000125, time_each_step=0.22s, eta=0:20:43\n",
      "2021-08-13 17:04:48 [INFO]\t[TRAIN] Epoch=25/100, Step=42/43, loss=12.515261, lr=0.000125, time_each_step=0.22s, eta=0:20:43\n",
      "2021-08-13 17:04:48 [INFO]\t[TRAIN] Epoch 25 finished, loss=13.253012, lr=0.000125 .\n",
      "2021-08-13 17:04:53 [INFO]\t[TRAIN] Epoch=26/100, Step=1/43, loss=8.904932, lr=0.000125, time_each_step=0.44s, eta=0:21:12\n",
      "2021-08-13 17:04:53 [INFO]\t[TRAIN] Epoch=26/100, Step=3/43, loss=11.167661, lr=0.000125, time_each_step=0.46s, eta=0:21:12\n",
      "2021-08-13 17:04:54 [INFO]\t[TRAIN] Epoch=26/100, Step=5/43, loss=18.606323, lr=0.000125, time_each_step=0.46s, eta=0:21:11\n",
      "2021-08-13 17:04:54 [INFO]\t[TRAIN] Epoch=26/100, Step=7/43, loss=11.023343, lr=0.000125, time_each_step=0.45s, eta=0:21:10\n",
      "2021-08-13 17:04:55 [INFO]\t[TRAIN] Epoch=26/100, Step=9/43, loss=11.999075, lr=0.000125, time_each_step=0.45s, eta=0:21:9\n",
      "2021-08-13 17:04:56 [INFO]\t[TRAIN] Epoch=26/100, Step=11/43, loss=12.364471, lr=0.000125, time_each_step=0.47s, eta=0:21:8\n",
      "2021-08-13 17:04:56 [INFO]\t[TRAIN] Epoch=26/100, Step=13/43, loss=11.824667, lr=0.000125, time_each_step=0.48s, eta=0:21:8\n",
      "2021-08-13 17:04:57 [INFO]\t[TRAIN] Epoch=26/100, Step=15/43, loss=13.797114, lr=0.000125, time_each_step=0.5s, eta=0:21:8\n",
      "2021-08-13 17:04:58 [INFO]\t[TRAIN] Epoch=26/100, Step=17/43, loss=14.480426, lr=0.000125, time_each_step=0.51s, eta=0:21:7\n",
      "2021-08-13 17:04:58 [INFO]\t[TRAIN] Epoch=26/100, Step=19/43, loss=15.83947, lr=0.000125, time_each_step=0.51s, eta=0:21:6\n",
      "2021-08-13 17:04:58 [INFO]\t[TRAIN] Epoch=26/100, Step=21/43, loss=12.028133, lr=0.000125, time_each_step=0.29s, eta=0:21:0\n",
      "2021-08-13 17:04:59 [INFO]\t[TRAIN] Epoch=26/100, Step=23/43, loss=8.716769, lr=0.000125, time_each_step=0.27s, eta=0:20:59\n",
      "2021-08-13 17:04:59 [INFO]\t[TRAIN] Epoch=26/100, Step=25/43, loss=15.884611, lr=0.000125, time_each_step=0.27s, eta=0:20:58\n",
      "2021-08-13 17:04:59 [INFO]\t[TRAIN] Epoch=26/100, Step=27/43, loss=9.112252, lr=0.000125, time_each_step=0.26s, eta=0:20:58\n",
      "2021-08-13 17:05:00 [INFO]\t[TRAIN] Epoch=26/100, Step=29/43, loss=12.908074, lr=0.000125, time_each_step=0.26s, eta=0:20:57\n",
      "2021-08-13 17:05:00 [INFO]\t[TRAIN] Epoch=26/100, Step=31/43, loss=10.108327, lr=0.000125, time_each_step=0.24s, eta=0:20:56\n",
      "2021-08-13 17:05:01 [INFO]\t[TRAIN] Epoch=26/100, Step=33/43, loss=9.012423, lr=0.000125, time_each_step=0.21s, eta=0:20:56\n",
      "2021-08-13 17:05:01 [INFO]\t[TRAIN] Epoch=26/100, Step=35/43, loss=13.356561, lr=0.000125, time_each_step=0.19s, eta=0:20:55\n",
      "2021-08-13 17:05:01 [INFO]\t[TRAIN] Epoch=26/100, Step=37/43, loss=13.499471, lr=0.000125, time_each_step=0.2s, eta=0:20:55\n",
      "2021-08-13 17:05:02 [INFO]\t[TRAIN] Epoch=26/100, Step=39/43, loss=14.588277, lr=0.000125, time_each_step=0.2s, eta=0:20:54\n",
      "2021-08-13 17:05:02 [INFO]\t[TRAIN] Epoch=26/100, Step=41/43, loss=15.221741, lr=0.000125, time_each_step=0.2s, eta=0:20:54\n",
      "2021-08-13 17:05:03 [INFO]\t[TRAIN] Epoch=26/100, Step=43/43, loss=14.41929, lr=0.000125, time_each_step=0.21s, eta=0:20:53\n",
      "2021-08-13 17:05:03 [INFO]\t[TRAIN] Epoch 26 finished, loss=12.466919, lr=0.000125 .\n",
      "2021-08-13 17:05:09 [INFO]\t[TRAIN] Epoch=27/100, Step=2/43, loss=10.055879, lr=0.000125, time_each_step=0.51s, eta=0:19:44\n",
      "2021-08-13 17:05:10 [INFO]\t[TRAIN] Epoch=27/100, Step=4/43, loss=14.211271, lr=0.000125, time_each_step=0.52s, eta=0:19:44\n",
      "2021-08-13 17:05:10 [INFO]\t[TRAIN] Epoch=27/100, Step=6/43, loss=9.569939, lr=0.000125, time_each_step=0.52s, eta=0:19:42\n",
      "2021-08-13 17:05:11 [INFO]\t[TRAIN] Epoch=27/100, Step=8/43, loss=12.385489, lr=0.000125, time_each_step=0.53s, eta=0:19:42\n",
      "2021-08-13 17:05:12 [INFO]\t[TRAIN] Epoch=27/100, Step=10/43, loss=12.417717, lr=0.000125, time_each_step=0.56s, eta=0:19:42\n",
      "2021-08-13 17:05:12 [INFO]\t[TRAIN] Epoch=27/100, Step=12/43, loss=16.286097, lr=0.000125, time_each_step=0.57s, eta=0:19:41\n",
      "2021-08-13 17:05:13 [INFO]\t[TRAIN] Epoch=27/100, Step=14/43, loss=12.075772, lr=0.000125, time_each_step=0.58s, eta=0:19:40\n",
      "2021-08-13 17:05:14 [INFO]\t[TRAIN] Epoch=27/100, Step=16/43, loss=14.285854, lr=0.000125, time_each_step=0.59s, eta=0:19:39\n",
      "2021-08-13 17:05:14 [INFO]\t[TRAIN] Epoch=27/100, Step=18/43, loss=12.268837, lr=0.000125, time_each_step=0.6s, eta=0:19:38\n",
      "2021-08-13 17:05:15 [INFO]\t[TRAIN] Epoch=27/100, Step=20/43, loss=16.614405, lr=0.000125, time_each_step=0.6s, eta=0:19:37\n",
      "2021-08-13 17:05:15 [INFO]\t[TRAIN] Epoch=27/100, Step=22/43, loss=20.057306, lr=0.000125, time_each_step=0.3s, eta=0:19:29\n",
      "2021-08-13 17:05:16 [INFO]\t[TRAIN] Epoch=27/100, Step=24/43, loss=12.324314, lr=0.000125, time_each_step=0.28s, eta=0:19:29\n",
      "2021-08-13 17:05:16 [INFO]\t[TRAIN] Epoch=27/100, Step=26/43, loss=15.895168, lr=0.000125, time_each_step=0.29s, eta=0:19:28\n",
      "2021-08-13 17:05:17 [INFO]\t[TRAIN] Epoch=27/100, Step=28/43, loss=11.665642, lr=0.000125, time_each_step=0.28s, eta=0:19:27\n",
      "2021-08-13 17:05:17 [INFO]\t[TRAIN] Epoch=27/100, Step=30/43, loss=9.906466, lr=0.000125, time_each_step=0.26s, eta=0:19:27\n",
      "2021-08-13 17:05:17 [INFO]\t[TRAIN] Epoch=27/100, Step=32/43, loss=15.411953, lr=0.000125, time_each_step=0.26s, eta=0:19:26\n",
      "2021-08-13 17:05:18 [INFO]\t[TRAIN] Epoch=27/100, Step=34/43, loss=11.071704, lr=0.000125, time_each_step=0.24s, eta=0:19:25\n",
      "2021-08-13 17:05:18 [INFO]\t[TRAIN] Epoch=27/100, Step=36/43, loss=14.458239, lr=0.000125, time_each_step=0.22s, eta=0:19:25\n",
      "2021-08-13 17:05:19 [INFO]\t[TRAIN] Epoch=27/100, Step=38/43, loss=11.36623, lr=0.000125, time_each_step=0.21s, eta=0:19:24\n",
      "2021-08-13 17:05:19 [INFO]\t[TRAIN] Epoch=27/100, Step=40/43, loss=11.045381, lr=0.000125, time_each_step=0.22s, eta=0:19:24\n",
      "2021-08-13 17:05:19 [INFO]\t[TRAIN] Epoch=27/100, Step=42/43, loss=11.645456, lr=0.000125, time_each_step=0.22s, eta=0:19:23\n",
      "2021-08-13 17:05:20 [INFO]\t[TRAIN] Epoch 27 finished, loss=13.126186, lr=0.000125 .\n",
      "2021-08-13 17:05:24 [INFO]\t[TRAIN] Epoch=28/100, Step=1/43, loss=8.228344, lr=0.000125, time_each_step=0.4s, eta=0:21:55\n",
      "2021-08-13 17:05:24 [INFO]\t[TRAIN] Epoch=28/100, Step=3/43, loss=8.81212, lr=0.000125, time_each_step=0.41s, eta=0:21:54\n",
      "2021-08-13 17:05:25 [INFO]\t[TRAIN] Epoch=28/100, Step=5/43, loss=9.970584, lr=0.000125, time_each_step=0.42s, eta=0:21:54\n",
      "2021-08-13 17:05:25 [INFO]\t[TRAIN] Epoch=28/100, Step=7/43, loss=12.38148, lr=0.000125, time_each_step=0.42s, eta=0:21:53\n",
      "2021-08-13 17:05:26 [INFO]\t[TRAIN] Epoch=28/100, Step=9/43, loss=14.096807, lr=0.000125, time_each_step=0.43s, eta=0:21:53\n",
      "2021-08-13 17:05:27 [INFO]\t[TRAIN] Epoch=28/100, Step=11/43, loss=14.654537, lr=0.000125, time_each_step=0.43s, eta=0:21:52\n",
      "2021-08-13 17:05:27 [INFO]\t[TRAIN] Epoch=28/100, Step=13/43, loss=9.457854, lr=0.000125, time_each_step=0.44s, eta=0:21:51\n",
      "2021-08-13 17:05:28 [INFO]\t[TRAIN] Epoch=28/100, Step=15/43, loss=11.565739, lr=0.000125, time_each_step=0.46s, eta=0:21:51\n",
      "2021-08-13 17:05:28 [INFO]\t[TRAIN] Epoch=28/100, Step=17/43, loss=14.606747, lr=0.000125, time_each_step=0.47s, eta=0:21:50\n",
      "2021-08-13 17:05:29 [INFO]\t[TRAIN] Epoch=28/100, Step=19/43, loss=14.295156, lr=0.000125, time_each_step=0.48s, eta=0:21:49\n",
      "2021-08-13 17:05:30 [INFO]\t[TRAIN] Epoch=28/100, Step=21/43, loss=10.130199, lr=0.000125, time_each_step=0.3s, eta=0:21:45\n",
      "2021-08-13 17:05:30 [INFO]\t[TRAIN] Epoch=28/100, Step=23/43, loss=11.880683, lr=0.000125, time_each_step=0.3s, eta=0:21:44\n",
      "2021-08-13 17:05:31 [INFO]\t[TRAIN] Epoch=28/100, Step=25/43, loss=12.090449, lr=0.000125, time_each_step=0.29s, eta=0:21:43\n",
      "2021-08-13 17:05:31 [INFO]\t[TRAIN] Epoch=28/100, Step=27/43, loss=12.102488, lr=0.000125, time_each_step=0.28s, eta=0:21:42\n",
      "2021-08-13 17:05:31 [INFO]\t[TRAIN] Epoch=28/100, Step=29/43, loss=12.33137, lr=0.000125, time_each_step=0.26s, eta=0:21:42\n",
      "2021-08-13 17:05:32 [INFO]\t[TRAIN] Epoch=28/100, Step=31/43, loss=16.140381, lr=0.000125, time_each_step=0.26s, eta=0:21:41\n",
      "2021-08-13 17:05:32 [INFO]\t[TRAIN] Epoch=28/100, Step=33/43, loss=9.961356, lr=0.000125, time_each_step=0.25s, eta=0:21:40\n",
      "2021-08-13 17:05:33 [INFO]\t[TRAIN] Epoch=28/100, Step=35/43, loss=8.831532, lr=0.000125, time_each_step=0.24s, eta=0:21:40\n",
      "2021-08-13 17:05:33 [INFO]\t[TRAIN] Epoch=28/100, Step=37/43, loss=11.645719, lr=0.000125, time_each_step=0.22s, eta=0:21:39\n",
      "2021-08-13 17:05:33 [INFO]\t[TRAIN] Epoch=28/100, Step=39/43, loss=13.696957, lr=0.000125, time_each_step=0.21s, eta=0:21:39\n",
      "2021-08-13 17:05:34 [INFO]\t[TRAIN] Epoch=28/100, Step=41/43, loss=10.063425, lr=0.000125, time_each_step=0.2s, eta=0:21:38\n",
      "2021-08-13 17:05:34 [INFO]\t[TRAIN] Epoch=28/100, Step=43/43, loss=11.939671, lr=0.000125, time_each_step=0.2s, eta=0:21:38\n",
      "2021-08-13 17:05:34 [INFO]\t[TRAIN] Epoch 28 finished, loss=11.646782, lr=0.000125 .\n",
      "2021-08-13 17:05:38 [INFO]\t[TRAIN] Epoch=29/100, Step=2/43, loss=15.454791, lr=0.000125, time_each_step=0.37s, eta=0:18:43\n",
      "2021-08-13 17:05:38 [INFO]\t[TRAIN] Epoch=29/100, Step=4/43, loss=9.224498, lr=0.000125, time_each_step=0.38s, eta=0:18:42\n",
      "2021-08-13 17:05:39 [INFO]\t[TRAIN] Epoch=29/100, Step=6/43, loss=11.650213, lr=0.000125, time_each_step=0.38s, eta=0:18:42\n",
      "2021-08-13 17:05:40 [INFO]\t[TRAIN] Epoch=29/100, Step=8/43, loss=12.103563, lr=0.000125, time_each_step=0.4s, eta=0:18:42\n",
      "2021-08-13 17:05:40 [INFO]\t[TRAIN] Epoch=29/100, Step=10/43, loss=10.817538, lr=0.000125, time_each_step=0.41s, eta=0:18:41\n",
      "2021-08-13 17:05:41 [INFO]\t[TRAIN] Epoch=29/100, Step=12/43, loss=10.042404, lr=0.000125, time_each_step=0.43s, eta=0:18:41\n",
      "2021-08-13 17:05:42 [INFO]\t[TRAIN] Epoch=29/100, Step=14/43, loss=15.865713, lr=0.000125, time_each_step=0.44s, eta=0:18:40\n",
      "2021-08-13 17:05:42 [INFO]\t[TRAIN] Epoch=29/100, Step=16/43, loss=11.401138, lr=0.000125, time_each_step=0.44s, eta=0:18:40\n",
      "2021-08-13 17:05:43 [INFO]\t[TRAIN] Epoch=29/100, Step=18/43, loss=14.037588, lr=0.000125, time_each_step=0.45s, eta=0:18:39\n",
      "2021-08-13 17:05:43 [INFO]\t[TRAIN] Epoch=29/100, Step=20/43, loss=14.161964, lr=0.000125, time_each_step=0.46s, eta=0:18:38\n",
      "2021-08-13 17:05:44 [INFO]\t[TRAIN] Epoch=29/100, Step=22/43, loss=20.894775, lr=0.000125, time_each_step=0.29s, eta=0:18:34\n",
      "2021-08-13 17:05:44 [INFO]\t[TRAIN] Epoch=29/100, Step=24/43, loss=10.265297, lr=0.000125, time_each_step=0.28s, eta=0:18:33\n",
      "2021-08-13 17:05:44 [INFO]\t[TRAIN] Epoch=29/100, Step=26/43, loss=12.616272, lr=0.000125, time_each_step=0.26s, eta=0:18:32\n",
      "2021-08-13 17:05:45 [INFO]\t[TRAIN] Epoch=29/100, Step=28/43, loss=10.905897, lr=0.000125, time_each_step=0.25s, eta=0:18:31\n",
      "2021-08-13 17:05:45 [INFO]\t[TRAIN] Epoch=29/100, Step=30/43, loss=13.190689, lr=0.000125, time_each_step=0.25s, eta=0:18:31\n",
      "2021-08-13 17:05:46 [INFO]\t[TRAIN] Epoch=29/100, Step=32/43, loss=12.513854, lr=0.000125, time_each_step=0.22s, eta=0:18:30\n",
      "2021-08-13 17:05:46 [INFO]\t[TRAIN] Epoch=29/100, Step=34/43, loss=12.417048, lr=0.000125, time_each_step=0.22s, eta=0:18:30\n",
      "2021-08-13 17:05:46 [INFO]\t[TRAIN] Epoch=29/100, Step=36/43, loss=6.388786, lr=0.000125, time_each_step=0.21s, eta=0:18:29\n",
      "2021-08-13 17:05:47 [INFO]\t[TRAIN] Epoch=29/100, Step=38/43, loss=12.113603, lr=0.000125, time_each_step=0.2s, eta=0:18:29\n",
      "2021-08-13 17:05:47 [INFO]\t[TRAIN] Epoch=29/100, Step=40/43, loss=13.510565, lr=0.000125, time_each_step=0.19s, eta=0:18:28\n",
      "2021-08-13 17:05:48 [INFO]\t[TRAIN] Epoch=29/100, Step=42/43, loss=10.718588, lr=0.000125, time_each_step=0.19s, eta=0:18:28\n",
      "2021-08-13 17:05:48 [INFO]\t[TRAIN] Epoch 29 finished, loss=13.114017, lr=0.000125 .\n",
      "2021-08-13 17:05:53 [INFO]\t[TRAIN] Epoch=30/100, Step=1/43, loss=9.694883, lr=0.000125, time_each_step=0.45s, eta=0:17:31\n",
      "2021-08-13 17:05:53 [INFO]\t[TRAIN] Epoch=30/100, Step=3/43, loss=15.813929, lr=0.000125, time_each_step=0.45s, eta=0:17:31\n",
      "2021-08-13 17:05:54 [INFO]\t[TRAIN] Epoch=30/100, Step=5/43, loss=7.460313, lr=0.000125, time_each_step=0.47s, eta=0:17:30\n",
      "2021-08-13 17:05:55 [INFO]\t[TRAIN] Epoch=30/100, Step=7/43, loss=11.325527, lr=0.000125, time_each_step=0.48s, eta=0:17:30\n",
      "2021-08-13 17:05:55 [INFO]\t[TRAIN] Epoch=30/100, Step=9/43, loss=7.851184, lr=0.000125, time_each_step=0.49s, eta=0:17:29\n",
      "2021-08-13 17:05:56 [INFO]\t[TRAIN] Epoch=30/100, Step=11/43, loss=11.577437, lr=0.000125, time_each_step=0.5s, eta=0:17:29\n",
      "2021-08-13 17:05:57 [INFO]\t[TRAIN] Epoch=30/100, Step=13/43, loss=9.889269, lr=0.000125, time_each_step=0.52s, eta=0:17:28\n",
      "2021-08-13 17:05:57 [INFO]\t[TRAIN] Epoch=30/100, Step=15/43, loss=9.1917, lr=0.000125, time_each_step=0.53s, eta=0:17:28\n",
      "2021-08-13 17:05:58 [INFO]\t[TRAIN] Epoch=30/100, Step=17/43, loss=15.02511, lr=0.000125, time_each_step=0.54s, eta=0:17:27\n",
      "2021-08-13 17:05:58 [INFO]\t[TRAIN] Epoch=30/100, Step=19/43, loss=13.639357, lr=0.000125, time_each_step=0.55s, eta=0:17:26\n",
      "2021-08-13 17:05:59 [INFO]\t[TRAIN] Epoch=30/100, Step=21/43, loss=13.331705, lr=0.000125, time_each_step=0.29s, eta=0:17:19\n",
      "2021-08-13 17:05:59 [INFO]\t[TRAIN] Epoch=30/100, Step=23/43, loss=13.391003, lr=0.000125, time_each_step=0.3s, eta=0:17:19\n",
      "2021-08-13 17:06:00 [INFO]\t[TRAIN] Epoch=30/100, Step=25/43, loss=18.11227, lr=0.000125, time_each_step=0.29s, eta=0:17:18\n",
      "2021-08-13 17:06:00 [INFO]\t[TRAIN] Epoch=30/100, Step=27/43, loss=10.264204, lr=0.000125, time_each_step=0.27s, eta=0:17:17\n",
      "2021-08-13 17:06:01 [INFO]\t[TRAIN] Epoch=30/100, Step=29/43, loss=8.780435, lr=0.000125, time_each_step=0.26s, eta=0:17:16\n",
      "2021-08-13 17:06:01 [INFO]\t[TRAIN] Epoch=30/100, Step=31/43, loss=15.296655, lr=0.000125, time_each_step=0.23s, eta=0:17:15\n",
      "2021-08-13 17:06:01 [INFO]\t[TRAIN] Epoch=30/100, Step=33/43, loss=8.349298, lr=0.000125, time_each_step=0.23s, eta=0:17:15\n",
      "2021-08-13 17:06:02 [INFO]\t[TRAIN] Epoch=30/100, Step=35/43, loss=12.869501, lr=0.000125, time_each_step=0.22s, eta=0:17:14\n",
      "2021-08-13 17:06:02 [INFO]\t[TRAIN] Epoch=30/100, Step=37/43, loss=14.57128, lr=0.000125, time_each_step=0.21s, eta=0:17:14\n",
      "2021-08-13 17:06:03 [INFO]\t[TRAIN] Epoch=30/100, Step=39/43, loss=10.732366, lr=0.000125, time_each_step=0.22s, eta=0:17:14\n",
      "2021-08-13 17:06:03 [INFO]\t[TRAIN] Epoch=30/100, Step=41/43, loss=11.79449, lr=0.000125, time_each_step=0.24s, eta=0:17:13\n",
      "2021-08-13 17:06:04 [INFO]\t[TRAIN] Epoch=30/100, Step=43/43, loss=12.962478, lr=0.000125, time_each_step=0.22s, eta=0:17:13\n",
      "2021-08-13 17:06:04 [INFO]\t[TRAIN] Epoch 30 finished, loss=12.334656, lr=0.000125 .\n",
      "2021-08-13 17:06:04 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:10<00:00,  1.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:06:15 [INFO]\t[EVAL] Finished, Epoch=30, bbox_map=29.52285 .\n",
      "2021-08-13 17:06:16 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:06:18 [INFO]\tModel saved in output/yolov3_darknet53/epoch_30.\n",
      "2021-08-13 17:06:18 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_30, bbox_map=29.522850453713062\n",
      "2021-08-13 17:06:22 [INFO]\t[TRAIN] Epoch=31/100, Step=2/43, loss=10.521128, lr=0.000125, time_each_step=0.41s, eta=0:20:30\n",
      "2021-08-13 17:06:23 [INFO]\t[TRAIN] Epoch=31/100, Step=4/43, loss=14.06498, lr=0.000125, time_each_step=0.42s, eta=0:20:29\n",
      "2021-08-13 17:06:23 [INFO]\t[TRAIN] Epoch=31/100, Step=6/43, loss=6.543252, lr=0.000125, time_each_step=0.43s, eta=0:20:29\n",
      "2021-08-13 17:06:24 [INFO]\t[TRAIN] Epoch=31/100, Step=8/43, loss=12.942621, lr=0.000125, time_each_step=0.45s, eta=0:20:28\n",
      "2021-08-13 17:06:24 [INFO]\t[TRAIN] Epoch=31/100, Step=10/43, loss=10.505842, lr=0.000125, time_each_step=0.45s, eta=0:20:28\n",
      "2021-08-13 17:06:25 [INFO]\t[TRAIN] Epoch=31/100, Step=12/43, loss=22.021643, lr=0.000125, time_each_step=0.46s, eta=0:20:27\n",
      "2021-08-13 17:06:26 [INFO]\t[TRAIN] Epoch=31/100, Step=14/43, loss=12.915173, lr=0.000125, time_each_step=0.47s, eta=0:20:26\n",
      "2021-08-13 17:06:26 [INFO]\t[TRAIN] Epoch=31/100, Step=16/43, loss=8.381725, lr=0.000125, time_each_step=0.49s, eta=0:20:26\n",
      "2021-08-13 17:06:27 [INFO]\t[TRAIN] Epoch=31/100, Step=18/43, loss=14.069679, lr=0.000125, time_each_step=0.48s, eta=0:20:25\n",
      "2021-08-13 17:06:27 [INFO]\t[TRAIN] Epoch=31/100, Step=20/43, loss=7.759333, lr=0.000125, time_each_step=0.49s, eta=0:20:24\n",
      "2021-08-13 17:06:28 [INFO]\t[TRAIN] Epoch=31/100, Step=22/43, loss=10.900124, lr=0.000125, time_each_step=0.3s, eta=0:20:19\n",
      "2021-08-13 17:06:28 [INFO]\t[TRAIN] Epoch=31/100, Step=24/43, loss=10.250225, lr=0.000125, time_each_step=0.29s, eta=0:20:18\n",
      "2021-08-13 17:06:29 [INFO]\t[TRAIN] Epoch=31/100, Step=26/43, loss=6.636539, lr=0.000125, time_each_step=0.29s, eta=0:20:18\n",
      "2021-08-13 17:06:29 [INFO]\t[TRAIN] Epoch=31/100, Step=28/43, loss=11.76683, lr=0.000125, time_each_step=0.28s, eta=0:20:17\n",
      "2021-08-13 17:06:30 [INFO]\t[TRAIN] Epoch=31/100, Step=30/43, loss=15.750923, lr=0.000125, time_each_step=0.28s, eta=0:20:16\n",
      "2021-08-13 17:06:30 [INFO]\t[TRAIN] Epoch=31/100, Step=32/43, loss=8.931122, lr=0.000125, time_each_step=0.26s, eta=0:20:16\n",
      "2021-08-13 17:06:31 [INFO]\t[TRAIN] Epoch=31/100, Step=34/43, loss=18.465935, lr=0.000125, time_each_step=0.25s, eta=0:20:15\n",
      "2021-08-13 17:06:31 [INFO]\t[TRAIN] Epoch=31/100, Step=36/43, loss=22.079351, lr=0.000125, time_each_step=0.23s, eta=0:20:14\n",
      "2021-08-13 17:06:32 [INFO]\t[TRAIN] Epoch=31/100, Step=38/43, loss=15.459892, lr=0.000125, time_each_step=0.24s, eta=0:20:14\n",
      "2021-08-13 17:06:32 [INFO]\t[TRAIN] Epoch=31/100, Step=40/43, loss=10.410897, lr=0.000125, time_each_step=0.24s, eta=0:20:13\n",
      "2021-08-13 17:06:32 [INFO]\t[TRAIN] Epoch=31/100, Step=42/43, loss=10.074551, lr=0.000125, time_each_step=0.22s, eta=0:20:13\n",
      "2021-08-13 17:06:33 [INFO]\t[TRAIN] Epoch 31 finished, loss=11.985374, lr=0.000125 .\n",
      "2021-08-13 17:06:36 [INFO]\t[TRAIN] Epoch=32/100, Step=1/43, loss=11.612019, lr=0.000125, time_each_step=0.4s, eta=0:18:55\n",
      "2021-08-13 17:06:37 [INFO]\t[TRAIN] Epoch=32/100, Step=3/43, loss=9.072843, lr=0.000125, time_each_step=0.42s, eta=0:18:55\n",
      "2021-08-13 17:06:38 [INFO]\t[TRAIN] Epoch=32/100, Step=5/43, loss=10.039827, lr=0.000125, time_each_step=0.42s, eta=0:18:54\n",
      "2021-08-13 17:06:38 [INFO]\t[TRAIN] Epoch=32/100, Step=7/43, loss=10.41822, lr=0.000125, time_each_step=0.43s, eta=0:18:53\n",
      "2021-08-13 17:06:39 [INFO]\t[TRAIN] Epoch=32/100, Step=9/43, loss=10.046648, lr=0.000125, time_each_step=0.45s, eta=0:18:53\n",
      "2021-08-13 17:06:40 [INFO]\t[TRAIN] Epoch=32/100, Step=11/43, loss=12.171112, lr=0.000125, time_each_step=0.45s, eta=0:18:52\n",
      "2021-08-13 17:06:40 [INFO]\t[TRAIN] Epoch=32/100, Step=13/43, loss=8.455572, lr=0.000125, time_each_step=0.46s, eta=0:18:52\n",
      "2021-08-13 17:06:41 [INFO]\t[TRAIN] Epoch=32/100, Step=15/43, loss=9.783007, lr=0.000125, time_each_step=0.47s, eta=0:18:51\n",
      "2021-08-13 17:06:42 [INFO]\t[TRAIN] Epoch=32/100, Step=17/43, loss=11.239218, lr=0.000125, time_each_step=0.48s, eta=0:18:50\n",
      "2021-08-13 17:06:42 [INFO]\t[TRAIN] Epoch=32/100, Step=19/43, loss=9.660608, lr=0.000125, time_each_step=0.49s, eta=0:18:50\n",
      "2021-08-13 17:06:43 [INFO]\t[TRAIN] Epoch=32/100, Step=21/43, loss=15.728451, lr=0.000125, time_each_step=0.3s, eta=0:18:45\n",
      "2021-08-13 17:06:43 [INFO]\t[TRAIN] Epoch=32/100, Step=23/43, loss=11.848361, lr=0.000125, time_each_step=0.29s, eta=0:18:44\n",
      "2021-08-13 17:06:43 [INFO]\t[TRAIN] Epoch=32/100, Step=25/43, loss=10.585983, lr=0.000125, time_each_step=0.29s, eta=0:18:43\n",
      "2021-08-13 17:06:44 [INFO]\t[TRAIN] Epoch=32/100, Step=27/43, loss=12.419455, lr=0.000125, time_each_step=0.27s, eta=0:18:42\n",
      "2021-08-13 17:06:44 [INFO]\t[TRAIN] Epoch=32/100, Step=29/43, loss=13.065702, lr=0.000125, time_each_step=0.26s, eta=0:18:41\n",
      "2021-08-13 17:06:45 [INFO]\t[TRAIN] Epoch=32/100, Step=31/43, loss=18.517591, lr=0.000125, time_each_step=0.25s, eta=0:18:41\n",
      "2021-08-13 17:06:45 [INFO]\t[TRAIN] Epoch=32/100, Step=33/43, loss=14.049582, lr=0.000125, time_each_step=0.24s, eta=0:18:40\n",
      "2021-08-13 17:06:45 [INFO]\t[TRAIN] Epoch=32/100, Step=35/43, loss=10.581752, lr=0.000125, time_each_step=0.21s, eta=0:18:40\n",
      "2021-08-13 17:06:46 [INFO]\t[TRAIN] Epoch=32/100, Step=37/43, loss=11.857691, lr=0.000125, time_each_step=0.21s, eta=0:18:39\n",
      "2021-08-13 17:06:46 [INFO]\t[TRAIN] Epoch=32/100, Step=39/43, loss=9.091893, lr=0.000125, time_each_step=0.21s, eta=0:18:39\n",
      "2021-08-13 17:06:47 [INFO]\t[TRAIN] Epoch=32/100, Step=41/43, loss=13.43361, lr=0.000125, time_each_step=0.21s, eta=0:18:38\n",
      "2021-08-13 17:06:47 [INFO]\t[TRAIN] Epoch=32/100, Step=43/43, loss=15.44488, lr=0.000125, time_each_step=0.22s, eta=0:18:38\n",
      "2021-08-13 17:06:47 [INFO]\t[TRAIN] Epoch 32 finished, loss=11.769518, lr=0.000125 .\n",
      "2021-08-13 17:06:57 [INFO]\t[TRAIN] Epoch=33/100, Step=2/43, loss=11.678459, lr=0.000125, time_each_step=0.69s, eta=0:18:23\n",
      "2021-08-13 17:06:58 [INFO]\t[TRAIN] Epoch=33/100, Step=4/43, loss=12.506268, lr=0.000125, time_each_step=0.7s, eta=0:18:22\n",
      "2021-08-13 17:06:59 [INFO]\t[TRAIN] Epoch=33/100, Step=6/43, loss=13.017462, lr=0.000125, time_each_step=0.71s, eta=0:18:21\n",
      "2021-08-13 17:06:59 [INFO]\t[TRAIN] Epoch=33/100, Step=8/43, loss=12.702243, lr=0.000125, time_each_step=0.72s, eta=0:18:20\n",
      "2021-08-13 17:07:00 [INFO]\t[TRAIN] Epoch=33/100, Step=10/43, loss=12.11664, lr=0.000125, time_each_step=0.75s, eta=0:18:19\n",
      "2021-08-13 17:07:01 [INFO]\t[TRAIN] Epoch=33/100, Step=12/43, loss=10.151819, lr=0.000125, time_each_step=0.76s, eta=0:18:18\n",
      "2021-08-13 17:07:01 [INFO]\t[TRAIN] Epoch=33/100, Step=14/43, loss=9.212813, lr=0.000125, time_each_step=0.75s, eta=0:18:17\n",
      "2021-08-13 17:07:02 [INFO]\t[TRAIN] Epoch=33/100, Step=16/43, loss=11.298303, lr=0.000125, time_each_step=0.77s, eta=0:18:16\n",
      "2021-08-13 17:07:02 [INFO]\t[TRAIN] Epoch=33/100, Step=18/43, loss=12.566641, lr=0.000125, time_each_step=0.78s, eta=0:18:14\n",
      "2021-08-13 17:07:03 [INFO]\t[TRAIN] Epoch=33/100, Step=20/43, loss=8.047594, lr=0.000125, time_each_step=0.77s, eta=0:18:13\n",
      "2021-08-13 17:07:03 [INFO]\t[TRAIN] Epoch=33/100, Step=22/43, loss=6.906999, lr=0.000125, time_each_step=0.3s, eta=0:18:1\n",
      "2021-08-13 17:07:04 [INFO]\t[TRAIN] Epoch=33/100, Step=24/43, loss=11.882381, lr=0.000125, time_each_step=0.28s, eta=0:18:0\n",
      "2021-08-13 17:07:04 [INFO]\t[TRAIN] Epoch=33/100, Step=26/43, loss=9.634104, lr=0.000125, time_each_step=0.27s, eta=0:17:59\n",
      "2021-08-13 17:07:04 [INFO]\t[TRAIN] Epoch=33/100, Step=28/43, loss=16.958508, lr=0.000125, time_each_step=0.26s, eta=0:17:59\n",
      "2021-08-13 17:07:05 [INFO]\t[TRAIN] Epoch=33/100, Step=30/43, loss=11.376545, lr=0.000125, time_each_step=0.23s, eta=0:17:58\n",
      "2021-08-13 17:07:05 [INFO]\t[TRAIN] Epoch=33/100, Step=32/43, loss=12.60158, lr=0.000125, time_each_step=0.23s, eta=0:17:57\n",
      "2021-08-13 17:07:06 [INFO]\t[TRAIN] Epoch=33/100, Step=34/43, loss=10.381873, lr=0.000125, time_each_step=0.23s, eta=0:17:57\n",
      "2021-08-13 17:07:06 [INFO]\t[TRAIN] Epoch=33/100, Step=36/43, loss=9.245865, lr=0.000125, time_each_step=0.22s, eta=0:17:56\n",
      "2021-08-13 17:07:06 [INFO]\t[TRAIN] Epoch=33/100, Step=38/43, loss=9.082895, lr=0.000125, time_each_step=0.21s, eta=0:17:56\n",
      "2021-08-13 17:07:07 [INFO]\t[TRAIN] Epoch=33/100, Step=40/43, loss=10.898792, lr=0.000125, time_each_step=0.21s, eta=0:17:55\n",
      "2021-08-13 17:07:07 [INFO]\t[TRAIN] Epoch=33/100, Step=42/43, loss=12.48284, lr=0.000125, time_each_step=0.2s, eta=0:17:55\n",
      "2021-08-13 17:07:07 [INFO]\t[TRAIN] Epoch 33 finished, loss=11.662177, lr=0.000125 .\n",
      "2021-08-13 17:07:12 [INFO]\t[TRAIN] Epoch=34/100, Step=1/43, loss=8.243081, lr=0.000125, time_each_step=0.41s, eta=0:24:4\n",
      "2021-08-13 17:07:12 [INFO]\t[TRAIN] Epoch=34/100, Step=3/43, loss=16.63097, lr=0.000125, time_each_step=0.43s, eta=0:24:4\n",
      "2021-08-13 17:07:13 [INFO]\t[TRAIN] Epoch=34/100, Step=5/43, loss=9.765491, lr=0.000125, time_each_step=0.45s, eta=0:24:4\n",
      "2021-08-13 17:07:14 [INFO]\t[TRAIN] Epoch=34/100, Step=7/43, loss=7.275953, lr=0.000125, time_each_step=0.47s, eta=0:24:4\n",
      "2021-08-13 17:07:14 [INFO]\t[TRAIN] Epoch=34/100, Step=9/43, loss=11.047736, lr=0.000125, time_each_step=0.46s, eta=0:24:3\n",
      "2021-08-13 17:07:15 [INFO]\t[TRAIN] Epoch=34/100, Step=11/43, loss=7.237872, lr=0.000125, time_each_step=0.47s, eta=0:24:2\n",
      "2021-08-13 17:07:16 [INFO]\t[TRAIN] Epoch=34/100, Step=13/43, loss=14.108152, lr=0.000125, time_each_step=0.49s, eta=0:24:1\n",
      "2021-08-13 17:07:16 [INFO]\t[TRAIN] Epoch=34/100, Step=15/43, loss=16.995777, lr=0.000125, time_each_step=0.49s, eta=0:24:1\n",
      "2021-08-13 17:07:17 [INFO]\t[TRAIN] Epoch=34/100, Step=17/43, loss=10.975447, lr=0.000125, time_each_step=0.5s, eta=0:24:0\n",
      "2021-08-13 17:07:17 [INFO]\t[TRAIN] Epoch=34/100, Step=19/43, loss=9.961739, lr=0.000125, time_each_step=0.5s, eta=0:23:59\n",
      "2021-08-13 17:07:18 [INFO]\t[TRAIN] Epoch=34/100, Step=21/43, loss=9.276718, lr=0.000125, time_each_step=0.29s, eta=0:23:53\n",
      "2021-08-13 17:07:18 [INFO]\t[TRAIN] Epoch=34/100, Step=23/43, loss=10.686808, lr=0.000125, time_each_step=0.28s, eta=0:23:52\n",
      "2021-08-13 17:07:19 [INFO]\t[TRAIN] Epoch=34/100, Step=25/43, loss=7.148746, lr=0.000125, time_each_step=0.26s, eta=0:23:52\n",
      "2021-08-13 17:07:19 [INFO]\t[TRAIN] Epoch=34/100, Step=27/43, loss=10.502944, lr=0.000125, time_each_step=0.25s, eta=0:23:51\n",
      "2021-08-13 17:07:20 [INFO]\t[TRAIN] Epoch=34/100, Step=29/43, loss=13.9729, lr=0.000125, time_each_step=0.26s, eta=0:23:51\n",
      "2021-08-13 17:07:20 [INFO]\t[TRAIN] Epoch=34/100, Step=31/43, loss=18.070625, lr=0.000125, time_each_step=0.25s, eta=0:23:50\n",
      "2021-08-13 17:07:20 [INFO]\t[TRAIN] Epoch=34/100, Step=33/43, loss=7.834805, lr=0.000125, time_each_step=0.24s, eta=0:23:49\n",
      "2021-08-13 17:07:21 [INFO]\t[TRAIN] Epoch=34/100, Step=35/43, loss=10.304016, lr=0.000125, time_each_step=0.22s, eta=0:23:49\n",
      "2021-08-13 17:07:21 [INFO]\t[TRAIN] Epoch=34/100, Step=37/43, loss=9.163223, lr=0.000125, time_each_step=0.23s, eta=0:23:48\n",
      "2021-08-13 17:07:22 [INFO]\t[TRAIN] Epoch=34/100, Step=39/43, loss=10.648302, lr=0.000125, time_each_step=0.23s, eta=0:23:48\n",
      "2021-08-13 17:07:22 [INFO]\t[TRAIN] Epoch=34/100, Step=41/43, loss=9.009232, lr=0.000125, time_each_step=0.23s, eta=0:23:47\n",
      "2021-08-13 17:07:23 [INFO]\t[TRAIN] Epoch=34/100, Step=43/43, loss=10.267914, lr=0.000125, time_each_step=0.23s, eta=0:23:47\n",
      "2021-08-13 17:07:23 [INFO]\t[TRAIN] Epoch 34 finished, loss=11.612173, lr=0.000125 .\n",
      "2021-08-13 17:07:27 [INFO]\t[TRAIN] Epoch=35/100, Step=2/43, loss=10.848349, lr=0.000125, time_each_step=0.43s, eta=0:18:23\n",
      "2021-08-13 17:07:28 [INFO]\t[TRAIN] Epoch=35/100, Step=4/43, loss=10.385477, lr=0.000125, time_each_step=0.44s, eta=0:18:22\n",
      "2021-08-13 17:07:28 [INFO]\t[TRAIN] Epoch=35/100, Step=6/43, loss=18.039324, lr=0.000125, time_each_step=0.44s, eta=0:18:21\n",
      "2021-08-13 17:07:29 [INFO]\t[TRAIN] Epoch=35/100, Step=8/43, loss=15.493237, lr=0.000125, time_each_step=0.45s, eta=0:18:21\n",
      "2021-08-13 17:07:30 [INFO]\t[TRAIN] Epoch=35/100, Step=10/43, loss=10.03487, lr=0.000125, time_each_step=0.46s, eta=0:18:20\n",
      "2021-08-13 17:07:30 [INFO]\t[TRAIN] Epoch=35/100, Step=12/43, loss=11.681341, lr=0.000125, time_each_step=0.48s, eta=0:18:20\n",
      "2021-08-13 17:07:31 [INFO]\t[TRAIN] Epoch=35/100, Step=14/43, loss=17.549877, lr=0.000125, time_each_step=0.49s, eta=0:18:19\n",
      "2021-08-13 17:07:32 [INFO]\t[TRAIN] Epoch=35/100, Step=16/43, loss=9.323885, lr=0.000125, time_each_step=0.49s, eta=0:18:18\n",
      "2021-08-13 17:07:32 [INFO]\t[TRAIN] Epoch=35/100, Step=18/43, loss=14.673487, lr=0.000125, time_each_step=0.51s, eta=0:18:18\n",
      "2021-08-13 17:07:33 [INFO]\t[TRAIN] Epoch=35/100, Step=20/43, loss=9.045947, lr=0.000125, time_each_step=0.51s, eta=0:18:17\n",
      "2021-08-13 17:07:33 [INFO]\t[TRAIN] Epoch=35/100, Step=22/43, loss=9.388231, lr=0.000125, time_each_step=0.3s, eta=0:18:11\n",
      "2021-08-13 17:07:34 [INFO]\t[TRAIN] Epoch=35/100, Step=24/43, loss=9.460202, lr=0.000125, time_each_step=0.3s, eta=0:18:11\n",
      "2021-08-13 17:07:35 [INFO]\t[TRAIN] Epoch=35/100, Step=26/43, loss=12.464063, lr=0.000125, time_each_step=0.31s, eta=0:18:10\n",
      "2021-08-13 17:07:35 [INFO]\t[TRAIN] Epoch=35/100, Step=28/43, loss=13.361899, lr=0.000125, time_each_step=0.31s, eta=0:18:10\n",
      "2021-08-13 17:07:35 [INFO]\t[TRAIN] Epoch=35/100, Step=30/43, loss=11.49059, lr=0.000125, time_each_step=0.28s, eta=0:18:9\n",
      "2021-08-13 17:07:36 [INFO]\t[TRAIN] Epoch=35/100, Step=32/43, loss=12.705194, lr=0.000125, time_each_step=0.26s, eta=0:18:8\n",
      "2021-08-13 17:07:36 [INFO]\t[TRAIN] Epoch=35/100, Step=34/43, loss=14.888965, lr=0.000125, time_each_step=0.24s, eta=0:18:7\n",
      "2021-08-13 17:07:36 [INFO]\t[TRAIN] Epoch=35/100, Step=36/43, loss=15.462171, lr=0.000125, time_each_step=0.24s, eta=0:18:7\n",
      "2021-08-13 17:07:37 [INFO]\t[TRAIN] Epoch=35/100, Step=38/43, loss=12.743077, lr=0.000125, time_each_step=0.23s, eta=0:18:6\n",
      "2021-08-13 17:07:38 [INFO]\t[TRAIN] Epoch=35/100, Step=40/43, loss=12.012152, lr=0.000125, time_each_step=0.24s, eta=0:18:6\n",
      "2021-08-13 17:07:38 [INFO]\t[TRAIN] Epoch=35/100, Step=42/43, loss=12.139237, lr=0.000125, time_each_step=0.23s, eta=0:18:5\n",
      "2021-08-13 17:07:38 [INFO]\t[TRAIN] Epoch 35 finished, loss=12.429504, lr=0.000125 .\n",
      "2021-08-13 17:07:41 [INFO]\t[TRAIN] Epoch=36/100, Step=1/43, loss=8.648994, lr=0.000125, time_each_step=0.38s, eta=0:18:18\n",
      "2021-08-13 17:07:43 [INFO]\t[TRAIN] Epoch=36/100, Step=3/43, loss=13.092821, lr=0.000125, time_each_step=0.41s, eta=0:18:18\n",
      "2021-08-13 17:07:43 [INFO]\t[TRAIN] Epoch=36/100, Step=5/43, loss=11.703791, lr=0.000125, time_each_step=0.42s, eta=0:18:18\n",
      "2021-08-13 17:07:44 [INFO]\t[TRAIN] Epoch=36/100, Step=7/43, loss=8.695614, lr=0.000125, time_each_step=0.44s, eta=0:18:17\n",
      "2021-08-13 17:07:45 [INFO]\t[TRAIN] Epoch=36/100, Step=9/43, loss=12.42766, lr=0.000125, time_each_step=0.45s, eta=0:18:17\n",
      "2021-08-13 17:07:45 [INFO]\t[TRAIN] Epoch=36/100, Step=11/43, loss=12.856228, lr=0.000125, time_each_step=0.46s, eta=0:18:16\n",
      "2021-08-13 17:07:46 [INFO]\t[TRAIN] Epoch=36/100, Step=13/43, loss=14.452469, lr=0.000125, time_each_step=0.47s, eta=0:18:16\n",
      "2021-08-13 17:07:46 [INFO]\t[TRAIN] Epoch=36/100, Step=15/43, loss=8.827597, lr=0.000125, time_each_step=0.47s, eta=0:18:15\n",
      "2021-08-13 17:07:47 [INFO]\t[TRAIN] Epoch=36/100, Step=17/43, loss=10.532331, lr=0.000125, time_each_step=0.48s, eta=0:18:14\n",
      "2021-08-13 17:07:48 [INFO]\t[TRAIN] Epoch=36/100, Step=19/43, loss=10.210516, lr=0.000125, time_each_step=0.5s, eta=0:18:14\n",
      "2021-08-13 17:07:49 [INFO]\t[TRAIN] Epoch=36/100, Step=21/43, loss=9.736116, lr=0.000125, time_each_step=0.36s, eta=0:18:9\n",
      "2021-08-13 17:07:49 [INFO]\t[TRAIN] Epoch=36/100, Step=23/43, loss=10.776349, lr=0.000125, time_each_step=0.31s, eta=0:18:8\n",
      "2021-08-13 17:07:49 [INFO]\t[TRAIN] Epoch=36/100, Step=25/43, loss=11.024496, lr=0.000125, time_each_step=0.3s, eta=0:18:7\n",
      "2021-08-13 17:07:50 [INFO]\t[TRAIN] Epoch=36/100, Step=27/43, loss=13.201733, lr=0.000125, time_each_step=0.29s, eta=0:18:6\n",
      "2021-08-13 17:07:50 [INFO]\t[TRAIN] Epoch=36/100, Step=29/43, loss=12.34547, lr=0.000125, time_each_step=0.29s, eta=0:18:6\n",
      "2021-08-13 17:07:51 [INFO]\t[TRAIN] Epoch=36/100, Step=31/43, loss=20.680557, lr=0.000125, time_each_step=0.28s, eta=0:18:5\n",
      "2021-08-13 17:07:51 [INFO]\t[TRAIN] Epoch=36/100, Step=33/43, loss=17.692936, lr=0.000125, time_each_step=0.28s, eta=0:18:4\n",
      "2021-08-13 17:07:52 [INFO]\t[TRAIN] Epoch=36/100, Step=35/43, loss=14.088207, lr=0.000125, time_each_step=0.27s, eta=0:18:4\n",
      "2021-08-13 17:07:52 [INFO]\t[TRAIN] Epoch=36/100, Step=37/43, loss=12.941787, lr=0.000125, time_each_step=0.26s, eta=0:18:3\n",
      "2021-08-13 17:07:53 [INFO]\t[TRAIN] Epoch=36/100, Step=39/43, loss=12.96733, lr=0.000125, time_each_step=0.24s, eta=0:18:3\n",
      "2021-08-13 17:07:53 [INFO]\t[TRAIN] Epoch=36/100, Step=41/43, loss=10.34746, lr=0.000125, time_each_step=0.22s, eta=0:18:2\n",
      "2021-08-13 17:07:54 [INFO]\t[TRAIN] Epoch=36/100, Step=43/43, loss=14.180675, lr=0.000125, time_each_step=0.23s, eta=0:18:2\n",
      "2021-08-13 17:07:54 [INFO]\t[TRAIN] Epoch 36 finished, loss=11.70691, lr=0.000125 .\n",
      "2021-08-13 17:07:59 [INFO]\t[TRAIN] Epoch=37/100, Step=2/43, loss=8.929743, lr=0.000125, time_each_step=0.46s, eta=0:18:19\n",
      "2021-08-13 17:07:59 [INFO]\t[TRAIN] Epoch=37/100, Step=4/43, loss=12.547626, lr=0.000125, time_each_step=0.47s, eta=0:18:19\n",
      "2021-08-13 17:08:00 [INFO]\t[TRAIN] Epoch=37/100, Step=6/43, loss=8.09119, lr=0.000125, time_each_step=0.49s, eta=0:18:19\n",
      "2021-08-13 17:08:01 [INFO]\t[TRAIN] Epoch=37/100, Step=8/43, loss=12.469519, lr=0.000125, time_each_step=0.51s, eta=0:18:19\n",
      "2021-08-13 17:08:02 [INFO]\t[TRAIN] Epoch=37/100, Step=10/43, loss=12.066317, lr=0.000125, time_each_step=0.51s, eta=0:18:18\n",
      "2021-08-13 17:08:02 [INFO]\t[TRAIN] Epoch=37/100, Step=12/43, loss=9.339182, lr=0.000125, time_each_step=0.51s, eta=0:18:17\n",
      "2021-08-13 17:08:03 [INFO]\t[TRAIN] Epoch=37/100, Step=14/43, loss=9.692671, lr=0.000125, time_each_step=0.52s, eta=0:18:16\n",
      "2021-08-13 17:08:03 [INFO]\t[TRAIN] Epoch=37/100, Step=16/43, loss=12.091713, lr=0.000125, time_each_step=0.54s, eta=0:18:15\n",
      "2021-08-13 17:08:04 [INFO]\t[TRAIN] Epoch=37/100, Step=18/43, loss=8.198956, lr=0.000125, time_each_step=0.55s, eta=0:18:14\n",
      "2021-08-13 17:08:04 [INFO]\t[TRAIN] Epoch=37/100, Step=20/43, loss=13.253374, lr=0.000125, time_each_step=0.54s, eta=0:18:13\n",
      "2021-08-13 17:08:05 [INFO]\t[TRAIN] Epoch=37/100, Step=22/43, loss=15.962319, lr=0.000125, time_each_step=0.31s, eta=0:18:7\n",
      "2021-08-13 17:08:05 [INFO]\t[TRAIN] Epoch=37/100, Step=24/43, loss=11.641081, lr=0.000125, time_each_step=0.3s, eta=0:18:6\n",
      "2021-08-13 17:08:06 [INFO]\t[TRAIN] Epoch=37/100, Step=26/43, loss=15.213319, lr=0.000125, time_each_step=0.27s, eta=0:18:5\n",
      "2021-08-13 17:08:06 [INFO]\t[TRAIN] Epoch=37/100, Step=28/43, loss=12.399113, lr=0.000125, time_each_step=0.25s, eta=0:18:5\n",
      "2021-08-13 17:08:06 [INFO]\t[TRAIN] Epoch=37/100, Step=30/43, loss=19.584026, lr=0.000125, time_each_step=0.25s, eta=0:18:4\n",
      "2021-08-13 17:08:07 [INFO]\t[TRAIN] Epoch=37/100, Step=32/43, loss=7.7008, lr=0.000125, time_each_step=0.24s, eta=0:18:3\n",
      "2021-08-13 17:08:07 [INFO]\t[TRAIN] Epoch=37/100, Step=34/43, loss=10.522768, lr=0.000125, time_each_step=0.22s, eta=0:18:3\n",
      "2021-08-13 17:08:08 [INFO]\t[TRAIN] Epoch=37/100, Step=36/43, loss=8.549909, lr=0.000125, time_each_step=0.21s, eta=0:18:2\n",
      "2021-08-13 17:08:08 [INFO]\t[TRAIN] Epoch=37/100, Step=38/43, loss=9.42757, lr=0.000125, time_each_step=0.2s, eta=0:18:2\n",
      "2021-08-13 17:08:08 [INFO]\t[TRAIN] Epoch=37/100, Step=40/43, loss=19.431736, lr=0.000125, time_each_step=0.2s, eta=0:18:1\n",
      "2021-08-13 17:08:09 [INFO]\t[TRAIN] Epoch=37/100, Step=42/43, loss=10.437374, lr=0.000125, time_each_step=0.2s, eta=0:18:1\n",
      "2021-08-13 17:08:09 [INFO]\t[TRAIN] Epoch 37 finished, loss=11.538177, lr=0.000125 .\n",
      "2021-08-13 17:08:14 [INFO]\t[TRAIN] Epoch=38/100, Step=1/43, loss=12.795307, lr=0.000125, time_each_step=0.43s, eta=0:17:40\n",
      "2021-08-13 17:08:15 [INFO]\t[TRAIN] Epoch=38/100, Step=3/43, loss=9.976734, lr=0.000125, time_each_step=0.45s, eta=0:17:40\n",
      "2021-08-13 17:08:15 [INFO]\t[TRAIN] Epoch=38/100, Step=5/43, loss=9.353935, lr=0.000125, time_each_step=0.46s, eta=0:17:39\n",
      "2021-08-13 17:08:16 [INFO]\t[TRAIN] Epoch=38/100, Step=7/43, loss=7.682698, lr=0.000125, time_each_step=0.48s, eta=0:17:39\n",
      "2021-08-13 17:08:17 [INFO]\t[TRAIN] Epoch=38/100, Step=9/43, loss=17.089111, lr=0.000125, time_each_step=0.49s, eta=0:17:39\n",
      "2021-08-13 17:08:17 [INFO]\t[TRAIN] Epoch=38/100, Step=11/43, loss=7.878599, lr=0.000125, time_each_step=0.5s, eta=0:17:38\n",
      "2021-08-13 17:08:18 [INFO]\t[TRAIN] Epoch=38/100, Step=13/43, loss=21.232834, lr=0.000125, time_each_step=0.51s, eta=0:17:37\n",
      "2021-08-13 17:08:18 [INFO]\t[TRAIN] Epoch=38/100, Step=15/43, loss=8.459523, lr=0.000125, time_each_step=0.52s, eta=0:17:36\n",
      "2021-08-13 17:08:19 [INFO]\t[TRAIN] Epoch=38/100, Step=17/43, loss=10.077677, lr=0.000125, time_each_step=0.52s, eta=0:17:35\n",
      "2021-08-13 17:08:19 [INFO]\t[TRAIN] Epoch=38/100, Step=19/43, loss=9.285665, lr=0.000125, time_each_step=0.52s, eta=0:17:34\n",
      "2021-08-13 17:08:20 [INFO]\t[TRAIN] Epoch=38/100, Step=21/43, loss=8.815846, lr=0.000125, time_each_step=0.3s, eta=0:17:28\n",
      "2021-08-13 17:08:21 [INFO]\t[TRAIN] Epoch=38/100, Step=23/43, loss=11.715725, lr=0.000125, time_each_step=0.29s, eta=0:17:28\n",
      "2021-08-13 17:08:21 [INFO]\t[TRAIN] Epoch=38/100, Step=25/43, loss=10.434899, lr=0.000125, time_each_step=0.28s, eta=0:17:27\n",
      "2021-08-13 17:08:21 [INFO]\t[TRAIN] Epoch=38/100, Step=27/43, loss=16.054676, lr=0.000125, time_each_step=0.26s, eta=0:17:26\n",
      "2021-08-13 17:08:21 [INFO]\t[TRAIN] Epoch=38/100, Step=29/43, loss=10.562009, lr=0.000125, time_each_step=0.24s, eta=0:17:25\n",
      "2021-08-13 17:08:22 [INFO]\t[TRAIN] Epoch=38/100, Step=31/43, loss=13.319368, lr=0.000125, time_each_step=0.24s, eta=0:17:25\n",
      "2021-08-13 17:08:22 [INFO]\t[TRAIN] Epoch=38/100, Step=33/43, loss=10.007236, lr=0.000125, time_each_step=0.23s, eta=0:17:24\n",
      "2021-08-13 17:08:23 [INFO]\t[TRAIN] Epoch=38/100, Step=35/43, loss=7.982123, lr=0.000125, time_each_step=0.22s, eta=0:17:24\n",
      "2021-08-13 17:08:23 [INFO]\t[TRAIN] Epoch=38/100, Step=37/43, loss=9.932426, lr=0.000125, time_each_step=0.21s, eta=0:17:23\n",
      "2021-08-13 17:08:23 [INFO]\t[TRAIN] Epoch=38/100, Step=39/43, loss=10.030445, lr=0.000125, time_each_step=0.21s, eta=0:17:23\n",
      "2021-08-13 17:08:24 [INFO]\t[TRAIN] Epoch=38/100, Step=41/43, loss=14.148589, lr=0.000125, time_each_step=0.19s, eta=0:17:22\n",
      "2021-08-13 17:08:24 [INFO]\t[TRAIN] Epoch=38/100, Step=43/43, loss=8.226714, lr=0.000125, time_each_step=0.17s, eta=0:17:22\n",
      "2021-08-13 17:08:24 [INFO]\t[TRAIN] Epoch 38 finished, loss=11.338352, lr=0.000125 .\n",
      "2021-08-13 17:08:28 [INFO]\t[TRAIN] Epoch=39/100, Step=2/43, loss=16.870493, lr=0.000125, time_each_step=0.36s, eta=0:17:8\n",
      "2021-08-13 17:08:29 [INFO]\t[TRAIN] Epoch=39/100, Step=4/43, loss=10.439329, lr=0.000125, time_each_step=0.38s, eta=0:17:8\n",
      "2021-08-13 17:08:30 [INFO]\t[TRAIN] Epoch=39/100, Step=6/43, loss=9.85137, lr=0.000125, time_each_step=0.4s, eta=0:17:8\n",
      "2021-08-13 17:08:30 [INFO]\t[TRAIN] Epoch=39/100, Step=8/43, loss=11.956072, lr=0.000125, time_each_step=0.4s, eta=0:17:8\n",
      "2021-08-13 17:08:31 [INFO]\t[TRAIN] Epoch=39/100, Step=10/43, loss=12.187307, lr=0.000125, time_each_step=0.42s, eta=0:17:7\n",
      "2021-08-13 17:08:31 [INFO]\t[TRAIN] Epoch=39/100, Step=12/43, loss=12.884316, lr=0.000125, time_each_step=0.44s, eta=0:17:7\n",
      "2021-08-13 17:08:32 [INFO]\t[TRAIN] Epoch=39/100, Step=14/43, loss=14.814752, lr=0.000125, time_each_step=0.46s, eta=0:17:7\n",
      "2021-08-13 17:08:33 [INFO]\t[TRAIN] Epoch=39/100, Step=16/43, loss=10.877919, lr=0.000125, time_each_step=0.48s, eta=0:17:6\n",
      "2021-08-13 17:08:34 [INFO]\t[TRAIN] Epoch=39/100, Step=18/43, loss=9.42272, lr=0.000125, time_each_step=0.49s, eta=0:17:6\n",
      "2021-08-13 17:08:34 [INFO]\t[TRAIN] Epoch=39/100, Step=20/43, loss=12.36105, lr=0.000125, time_each_step=0.52s, eta=0:17:5\n",
      "2021-08-13 17:08:35 [INFO]\t[TRAIN] Epoch=39/100, Step=22/43, loss=10.154819, lr=0.000125, time_each_step=0.34s, eta=0:17:1\n",
      "2021-08-13 17:08:35 [INFO]\t[TRAIN] Epoch=39/100, Step=24/43, loss=11.778753, lr=0.000125, time_each_step=0.32s, eta=0:17:0\n",
      "2021-08-13 17:08:36 [INFO]\t[TRAIN] Epoch=39/100, Step=26/43, loss=14.369636, lr=0.000125, time_each_step=0.31s, eta=0:16:59\n",
      "2021-08-13 17:08:36 [INFO]\t[TRAIN] Epoch=39/100, Step=28/43, loss=12.50762, lr=0.000125, time_each_step=0.3s, eta=0:16:58\n",
      "2021-08-13 17:08:37 [INFO]\t[TRAIN] Epoch=39/100, Step=30/43, loss=9.825432, lr=0.000125, time_each_step=0.3s, eta=0:16:57\n",
      "2021-08-13 17:08:37 [INFO]\t[TRAIN] Epoch=39/100, Step=32/43, loss=21.061298, lr=0.000125, time_each_step=0.28s, eta=0:16:57\n",
      "2021-08-13 17:08:38 [INFO]\t[TRAIN] Epoch=39/100, Step=34/43, loss=8.357605, lr=0.000125, time_each_step=0.27s, eta=0:16:56\n",
      "2021-08-13 17:08:38 [INFO]\t[TRAIN] Epoch=39/100, Step=36/43, loss=15.295547, lr=0.000125, time_each_step=0.25s, eta=0:16:55\n",
      "2021-08-13 17:08:38 [INFO]\t[TRAIN] Epoch=39/100, Step=38/43, loss=13.357864, lr=0.000125, time_each_step=0.24s, eta=0:16:55\n",
      "2021-08-13 17:08:39 [INFO]\t[TRAIN] Epoch=39/100, Step=40/43, loss=10.196703, lr=0.000125, time_each_step=0.23s, eta=0:16:54\n",
      "2021-08-13 17:08:40 [INFO]\t[TRAIN] Epoch=39/100, Step=42/43, loss=11.000988, lr=0.000125, time_each_step=0.23s, eta=0:16:54\n",
      "2021-08-13 17:08:40 [INFO]\t[TRAIN] Epoch 39 finished, loss=12.338893, lr=0.000125 .\n",
      "2021-08-13 17:08:45 [INFO]\t[TRAIN] Epoch=40/100, Step=1/43, loss=13.669018, lr=0.000125, time_each_step=0.48s, eta=0:17:50\n",
      "2021-08-13 17:08:45 [INFO]\t[TRAIN] Epoch=40/100, Step=3/43, loss=12.52916, lr=0.000125, time_each_step=0.49s, eta=0:17:49\n",
      "2021-08-13 17:08:46 [INFO]\t[TRAIN] Epoch=40/100, Step=5/43, loss=13.059427, lr=0.000125, time_each_step=0.49s, eta=0:17:48\n",
      "2021-08-13 17:08:47 [INFO]\t[TRAIN] Epoch=40/100, Step=7/43, loss=11.08099, lr=0.000125, time_each_step=0.5s, eta=0:17:47\n",
      "2021-08-13 17:08:47 [INFO]\t[TRAIN] Epoch=40/100, Step=9/43, loss=9.003379, lr=0.000125, time_each_step=0.5s, eta=0:17:46\n",
      "2021-08-13 17:08:48 [INFO]\t[TRAIN] Epoch=40/100, Step=11/43, loss=12.910799, lr=0.000125, time_each_step=0.5s, eta=0:17:46\n",
      "2021-08-13 17:08:48 [INFO]\t[TRAIN] Epoch=40/100, Step=13/43, loss=11.446671, lr=0.000125, time_each_step=0.53s, eta=0:17:45\n",
      "2021-08-13 17:08:49 [INFO]\t[TRAIN] Epoch=40/100, Step=15/43, loss=14.977898, lr=0.000125, time_each_step=0.53s, eta=0:17:44\n",
      "2021-08-13 17:08:50 [INFO]\t[TRAIN] Epoch=40/100, Step=17/43, loss=9.228714, lr=0.000125, time_each_step=0.55s, eta=0:17:44\n",
      "2021-08-13 17:08:50 [INFO]\t[TRAIN] Epoch=40/100, Step=19/43, loss=8.684447, lr=0.000125, time_each_step=0.54s, eta=0:17:42\n",
      "2021-08-13 17:08:51 [INFO]\t[TRAIN] Epoch=40/100, Step=21/43, loss=8.986007, lr=0.000125, time_each_step=0.31s, eta=0:17:36\n",
      "2021-08-13 17:08:51 [INFO]\t[TRAIN] Epoch=40/100, Step=23/43, loss=17.095366, lr=0.000125, time_each_step=0.3s, eta=0:17:35\n",
      "2021-08-13 17:08:52 [INFO]\t[TRAIN] Epoch=40/100, Step=25/43, loss=10.528577, lr=0.000125, time_each_step=0.3s, eta=0:17:35\n",
      "2021-08-13 17:08:52 [INFO]\t[TRAIN] Epoch=40/100, Step=27/43, loss=10.743771, lr=0.000125, time_each_step=0.28s, eta=0:17:34\n",
      "2021-08-13 17:08:52 [INFO]\t[TRAIN] Epoch=40/100, Step=29/43, loss=8.482992, lr=0.000125, time_each_step=0.26s, eta=0:17:33\n",
      "2021-08-13 17:08:53 [INFO]\t[TRAIN] Epoch=40/100, Step=31/43, loss=11.961074, lr=0.000125, time_each_step=0.26s, eta=0:17:33\n",
      "2021-08-13 17:08:53 [INFO]\t[TRAIN] Epoch=40/100, Step=33/43, loss=12.226358, lr=0.000125, time_each_step=0.24s, eta=0:17:32\n",
      "2021-08-13 17:08:54 [INFO]\t[TRAIN] Epoch=40/100, Step=35/43, loss=10.265766, lr=0.000125, time_each_step=0.23s, eta=0:17:31\n",
      "2021-08-13 17:08:54 [INFO]\t[TRAIN] Epoch=40/100, Step=37/43, loss=7.591941, lr=0.000125, time_each_step=0.21s, eta=0:17:31\n",
      "2021-08-13 17:08:54 [INFO]\t[TRAIN] Epoch=40/100, Step=39/43, loss=10.812029, lr=0.000125, time_each_step=0.21s, eta=0:17:30\n",
      "2021-08-13 17:08:55 [INFO]\t[TRAIN] Epoch=40/100, Step=41/43, loss=10.125527, lr=0.000125, time_each_step=0.19s, eta=0:17:30\n",
      "2021-08-13 17:08:55 [INFO]\t[TRAIN] Epoch=40/100, Step=43/43, loss=11.832111, lr=0.000125, time_each_step=0.19s, eta=0:17:29\n",
      "2021-08-13 17:08:55 [INFO]\t[TRAIN] Epoch 40 finished, loss=11.391555, lr=0.000125 .\n",
      "2021-08-13 17:08:55 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:05<00:00,  2.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:09:01 [INFO]\t[EVAL] Finished, Epoch=40, bbox_map=44.336376 .\n",
      "2021-08-13 17:09:03 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:09:04 [INFO]\tModel saved in output/yolov3_darknet53/epoch_40.\n",
      "2021-08-13 17:09:04 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_40, bbox_map=44.33637632129236\n",
      "2021-08-13 17:09:10 [INFO]\t[TRAIN] Epoch=41/100, Step=2/43, loss=8.148412, lr=0.000125, time_each_step=0.47s, eta=0:16:18\n",
      "2021-08-13 17:09:11 [INFO]\t[TRAIN] Epoch=41/100, Step=4/43, loss=10.348181, lr=0.000125, time_each_step=0.48s, eta=0:16:18\n",
      "2021-08-13 17:09:12 [INFO]\t[TRAIN] Epoch=41/100, Step=6/43, loss=11.896499, lr=0.000125, time_each_step=0.52s, eta=0:16:18\n",
      "2021-08-13 17:09:12 [INFO]\t[TRAIN] Epoch=41/100, Step=8/43, loss=8.042884, lr=0.000125, time_each_step=0.52s, eta=0:16:17\n",
      "2021-08-13 17:09:13 [INFO]\t[TRAIN] Epoch=41/100, Step=10/43, loss=12.345535, lr=0.000125, time_each_step=0.54s, eta=0:16:17\n",
      "2021-08-13 17:09:14 [INFO]\t[TRAIN] Epoch=41/100, Step=12/43, loss=8.855793, lr=0.000125, time_each_step=0.56s, eta=0:16:16\n",
      "2021-08-13 17:09:14 [INFO]\t[TRAIN] Epoch=41/100, Step=14/43, loss=8.11678, lr=0.000125, time_each_step=0.56s, eta=0:16:15\n",
      "2021-08-13 17:09:15 [INFO]\t[TRAIN] Epoch=41/100, Step=16/43, loss=10.012857, lr=0.000125, time_each_step=0.57s, eta=0:16:15\n",
      "2021-08-13 17:09:15 [INFO]\t[TRAIN] Epoch=41/100, Step=18/43, loss=9.039287, lr=0.000125, time_each_step=0.58s, eta=0:16:14\n",
      "2021-08-13 17:09:16 [INFO]\t[TRAIN] Epoch=41/100, Step=20/43, loss=8.176746, lr=0.000125, time_each_step=0.59s, eta=0:16:13\n",
      "2021-08-13 17:09:16 [INFO]\t[TRAIN] Epoch=41/100, Step=22/43, loss=12.952975, lr=0.000125, time_each_step=0.31s, eta=0:16:6\n",
      "2021-08-13 17:09:17 [INFO]\t[TRAIN] Epoch=41/100, Step=24/43, loss=12.894108, lr=0.000125, time_each_step=0.29s, eta=0:16:5\n",
      "2021-08-13 17:09:17 [INFO]\t[TRAIN] Epoch=41/100, Step=26/43, loss=14.148725, lr=0.000125, time_each_step=0.26s, eta=0:16:4\n",
      "2021-08-13 17:09:17 [INFO]\t[TRAIN] Epoch=41/100, Step=28/43, loss=11.917692, lr=0.000125, time_each_step=0.25s, eta=0:16:3\n",
      "2021-08-13 17:09:18 [INFO]\t[TRAIN] Epoch=41/100, Step=30/43, loss=6.705065, lr=0.000125, time_each_step=0.24s, eta=0:16:2\n",
      "2021-08-13 17:09:18 [INFO]\t[TRAIN] Epoch=41/100, Step=32/43, loss=11.148835, lr=0.000125, time_each_step=0.23s, eta=0:16:2\n",
      "2021-08-13 17:09:19 [INFO]\t[TRAIN] Epoch=41/100, Step=34/43, loss=11.531219, lr=0.000125, time_each_step=0.23s, eta=0:16:1\n",
      "2021-08-13 17:09:19 [INFO]\t[TRAIN] Epoch=41/100, Step=36/43, loss=14.226255, lr=0.000125, time_each_step=0.2s, eta=0:16:1\n",
      "2021-08-13 17:09:19 [INFO]\t[TRAIN] Epoch=41/100, Step=38/43, loss=8.474151, lr=0.000125, time_each_step=0.2s, eta=0:16:0\n",
      "2021-08-13 17:09:20 [INFO]\t[TRAIN] Epoch=41/100, Step=40/43, loss=9.980556, lr=0.000125, time_each_step=0.2s, eta=0:16:0\n",
      "2021-08-13 17:09:20 [INFO]\t[TRAIN] Epoch=41/100, Step=42/43, loss=16.367405, lr=0.000125, time_each_step=0.21s, eta=0:15:59\n",
      "2021-08-13 17:09:20 [INFO]\t[TRAIN] Epoch 41 finished, loss=10.944005, lr=0.000125 .\n",
      "2021-08-13 17:09:24 [INFO]\t[TRAIN] Epoch=42/100, Step=1/43, loss=8.092957, lr=0.000125, time_each_step=0.39s, eta=0:17:2\n",
      "2021-08-13 17:09:25 [INFO]\t[TRAIN] Epoch=42/100, Step=3/43, loss=10.14289, lr=0.000125, time_each_step=0.4s, eta=0:17:1\n",
      "2021-08-13 17:09:26 [INFO]\t[TRAIN] Epoch=42/100, Step=5/43, loss=10.84931, lr=0.000125, time_each_step=0.41s, eta=0:17:1\n",
      "2021-08-13 17:09:26 [INFO]\t[TRAIN] Epoch=42/100, Step=7/43, loss=11.943421, lr=0.000125, time_each_step=0.43s, eta=0:17:1\n",
      "2021-08-13 17:09:27 [INFO]\t[TRAIN] Epoch=42/100, Step=9/43, loss=15.983068, lr=0.000125, time_each_step=0.45s, eta=0:17:0\n",
      "2021-08-13 17:09:28 [INFO]\t[TRAIN] Epoch=42/100, Step=11/43, loss=11.201756, lr=0.000125, time_each_step=0.45s, eta=0:17:0\n",
      "2021-08-13 17:09:28 [INFO]\t[TRAIN] Epoch=42/100, Step=13/43, loss=8.146687, lr=0.000125, time_each_step=0.48s, eta=0:17:0\n",
      "2021-08-13 17:09:29 [INFO]\t[TRAIN] Epoch=42/100, Step=15/43, loss=10.067567, lr=0.000125, time_each_step=0.48s, eta=0:16:59\n",
      "2021-08-13 17:09:30 [INFO]\t[TRAIN] Epoch=42/100, Step=17/43, loss=10.166569, lr=0.000125, time_each_step=0.49s, eta=0:16:58\n",
      "2021-08-13 17:09:30 [INFO]\t[TRAIN] Epoch=42/100, Step=19/43, loss=11.162766, lr=0.000125, time_each_step=0.5s, eta=0:16:57\n",
      "2021-08-13 17:09:31 [INFO]\t[TRAIN] Epoch=42/100, Step=21/43, loss=9.65276, lr=0.000125, time_each_step=0.31s, eta=0:16:52\n",
      "2021-08-13 17:09:31 [INFO]\t[TRAIN] Epoch=42/100, Step=23/43, loss=10.649557, lr=0.000125, time_each_step=0.3s, eta=0:16:51\n",
      "2021-08-13 17:09:32 [INFO]\t[TRAIN] Epoch=42/100, Step=25/43, loss=11.812823, lr=0.000125, time_each_step=0.3s, eta=0:16:51\n",
      "2021-08-13 17:09:32 [INFO]\t[TRAIN] Epoch=42/100, Step=27/43, loss=13.196331, lr=0.000125, time_each_step=0.28s, eta=0:16:50\n",
      "2021-08-13 17:09:32 [INFO]\t[TRAIN] Epoch=42/100, Step=29/43, loss=13.097079, lr=0.000125, time_each_step=0.26s, eta=0:16:49\n",
      "2021-08-13 17:09:33 [INFO]\t[TRAIN] Epoch=42/100, Step=31/43, loss=15.042433, lr=0.000125, time_each_step=0.26s, eta=0:16:48\n",
      "2021-08-13 17:09:33 [INFO]\t[TRAIN] Epoch=42/100, Step=33/43, loss=9.327627, lr=0.000125, time_each_step=0.23s, eta=0:16:48\n",
      "2021-08-13 17:09:33 [INFO]\t[TRAIN] Epoch=42/100, Step=35/43, loss=12.564684, lr=0.000125, time_each_step=0.22s, eta=0:16:47\n",
      "2021-08-13 17:09:34 [INFO]\t[TRAIN] Epoch=42/100, Step=37/43, loss=10.803701, lr=0.000125, time_each_step=0.2s, eta=0:16:47\n",
      "2021-08-13 17:09:34 [INFO]\t[TRAIN] Epoch=42/100, Step=39/43, loss=23.482498, lr=0.000125, time_each_step=0.21s, eta=0:16:46\n",
      "2021-08-13 17:09:35 [INFO]\t[TRAIN] Epoch=42/100, Step=41/43, loss=12.056827, lr=0.000125, time_each_step=0.19s, eta=0:16:46\n",
      "2021-08-13 17:09:35 [INFO]\t[TRAIN] Epoch=42/100, Step=43/43, loss=9.830398, lr=0.000125, time_each_step=0.2s, eta=0:16:45\n",
      "2021-08-13 17:09:35 [INFO]\t[TRAIN] Epoch 42 finished, loss=11.608794, lr=0.000125 .\n",
      "2021-08-13 17:09:44 [INFO]\t[TRAIN] Epoch=43/100, Step=2/43, loss=12.531918, lr=0.000125, time_each_step=0.6s, eta=0:15:5\n",
      "2021-08-13 17:09:44 [INFO]\t[TRAIN] Epoch=43/100, Step=4/43, loss=10.69106, lr=0.000125, time_each_step=0.62s, eta=0:15:5\n",
      "2021-08-13 17:09:45 [INFO]\t[TRAIN] Epoch=43/100, Step=6/43, loss=8.812462, lr=0.000125, time_each_step=0.64s, eta=0:15:4\n",
      "2021-08-13 17:09:46 [INFO]\t[TRAIN] Epoch=43/100, Step=8/43, loss=10.872855, lr=0.000125, time_each_step=0.65s, eta=0:15:3\n",
      "2021-08-13 17:09:46 [INFO]\t[TRAIN] Epoch=43/100, Step=10/43, loss=16.163351, lr=0.000125, time_each_step=0.67s, eta=0:15:2\n",
      "2021-08-13 17:09:47 [INFO]\t[TRAIN] Epoch=43/100, Step=12/43, loss=16.677917, lr=0.000125, time_each_step=0.67s, eta=0:15:1\n",
      "2021-08-13 17:09:48 [INFO]\t[TRAIN] Epoch=43/100, Step=14/43, loss=13.783091, lr=0.000125, time_each_step=0.69s, eta=0:15:0\n",
      "2021-08-13 17:09:48 [INFO]\t[TRAIN] Epoch=43/100, Step=16/43, loss=10.810551, lr=0.000125, time_each_step=0.7s, eta=0:14:59\n",
      "2021-08-13 17:09:49 [INFO]\t[TRAIN] Epoch=43/100, Step=18/43, loss=12.178417, lr=0.000125, time_each_step=0.71s, eta=0:14:58\n",
      "2021-08-13 17:09:49 [INFO]\t[TRAIN] Epoch=43/100, Step=20/43, loss=15.057442, lr=0.000125, time_each_step=0.72s, eta=0:14:57\n",
      "2021-08-13 17:09:50 [INFO]\t[TRAIN] Epoch=43/100, Step=22/43, loss=7.506724, lr=0.000125, time_each_step=0.31s, eta=0:14:47\n",
      "2021-08-13 17:09:50 [INFO]\t[TRAIN] Epoch=43/100, Step=24/43, loss=8.15264, lr=0.000125, time_each_step=0.29s, eta=0:14:46\n",
      "2021-08-13 17:09:51 [INFO]\t[TRAIN] Epoch=43/100, Step=26/43, loss=12.395617, lr=0.000125, time_each_step=0.27s, eta=0:14:45\n",
      "2021-08-13 17:09:51 [INFO]\t[TRAIN] Epoch=43/100, Step=28/43, loss=6.913574, lr=0.000125, time_each_step=0.26s, eta=0:14:44\n",
      "2021-08-13 17:09:51 [INFO]\t[TRAIN] Epoch=43/100, Step=30/43, loss=9.946276, lr=0.000125, time_each_step=0.25s, eta=0:14:44\n",
      "2021-08-13 17:09:52 [INFO]\t[TRAIN] Epoch=43/100, Step=32/43, loss=9.327406, lr=0.000125, time_each_step=0.24s, eta=0:14:43\n",
      "2021-08-13 17:09:52 [INFO]\t[TRAIN] Epoch=43/100, Step=34/43, loss=12.550776, lr=0.000125, time_each_step=0.22s, eta=0:14:42\n",
      "2021-08-13 17:09:52 [INFO]\t[TRAIN] Epoch=43/100, Step=36/43, loss=13.964547, lr=0.000125, time_each_step=0.2s, eta=0:14:42\n",
      "2021-08-13 17:09:53 [INFO]\t[TRAIN] Epoch=43/100, Step=38/43, loss=10.188918, lr=0.000125, time_each_step=0.21s, eta=0:14:41\n",
      "2021-08-13 17:09:53 [INFO]\t[TRAIN] Epoch=43/100, Step=40/43, loss=11.088552, lr=0.000125, time_each_step=0.2s, eta=0:14:41\n",
      "2021-08-13 17:09:54 [INFO]\t[TRAIN] Epoch=43/100, Step=42/43, loss=11.561853, lr=0.000125, time_each_step=0.19s, eta=0:14:41\n",
      "2021-08-13 17:09:54 [INFO]\t[TRAIN] Epoch 43 finished, loss=10.946003, lr=0.000125 .\n",
      "2021-08-13 17:09:59 [INFO]\t[TRAIN] Epoch=44/100, Step=1/43, loss=8.737079, lr=0.000125, time_each_step=0.47s, eta=0:18:39\n",
      "2021-08-13 17:10:00 [INFO]\t[TRAIN] Epoch=44/100, Step=3/43, loss=10.007922, lr=0.000125, time_each_step=0.49s, eta=0:18:39\n",
      "2021-08-13 17:10:01 [INFO]\t[TRAIN] Epoch=44/100, Step=5/43, loss=9.701988, lr=0.000125, time_each_step=0.49s, eta=0:18:38\n",
      "2021-08-13 17:10:01 [INFO]\t[TRAIN] Epoch=44/100, Step=7/43, loss=9.174434, lr=0.000125, time_each_step=0.49s, eta=0:18:37\n",
      "2021-08-13 17:10:02 [INFO]\t[TRAIN] Epoch=44/100, Step=9/43, loss=11.217954, lr=0.000125, time_each_step=0.52s, eta=0:18:37\n",
      "2021-08-13 17:10:03 [INFO]\t[TRAIN] Epoch=44/100, Step=11/43, loss=10.944138, lr=0.000125, time_each_step=0.54s, eta=0:18:37\n",
      "2021-08-13 17:10:03 [INFO]\t[TRAIN] Epoch=44/100, Step=13/43, loss=13.604634, lr=0.000125, time_each_step=0.55s, eta=0:18:36\n",
      "2021-08-13 17:10:04 [INFO]\t[TRAIN] Epoch=44/100, Step=15/43, loss=11.848791, lr=0.000125, time_each_step=0.55s, eta=0:18:35\n",
      "2021-08-13 17:10:05 [INFO]\t[TRAIN] Epoch=44/100, Step=17/43, loss=11.867697, lr=0.000125, time_each_step=0.56s, eta=0:18:34\n",
      "2021-08-13 17:10:05 [INFO]\t[TRAIN] Epoch=44/100, Step=19/43, loss=10.77088, lr=0.000125, time_each_step=0.57s, eta=0:18:33\n",
      "2021-08-13 17:10:06 [INFO]\t[TRAIN] Epoch=44/100, Step=21/43, loss=10.185511, lr=0.000125, time_each_step=0.31s, eta=0:18:26\n",
      "2021-08-13 17:10:06 [INFO]\t[TRAIN] Epoch=44/100, Step=23/43, loss=12.241461, lr=0.000125, time_each_step=0.29s, eta=0:18:25\n",
      "2021-08-13 17:10:07 [INFO]\t[TRAIN] Epoch=44/100, Step=25/43, loss=8.549535, lr=0.000125, time_each_step=0.31s, eta=0:18:25\n",
      "2021-08-13 17:10:07 [INFO]\t[TRAIN] Epoch=44/100, Step=27/43, loss=10.127547, lr=0.000125, time_each_step=0.29s, eta=0:18:24\n",
      "2021-08-13 17:10:08 [INFO]\t[TRAIN] Epoch=44/100, Step=29/43, loss=8.484175, lr=0.000125, time_each_step=0.29s, eta=0:18:24\n",
      "2021-08-13 17:10:08 [INFO]\t[TRAIN] Epoch=44/100, Step=31/43, loss=12.049688, lr=0.000125, time_each_step=0.28s, eta=0:18:23\n",
      "2021-08-13 17:10:09 [INFO]\t[TRAIN] Epoch=44/100, Step=33/43, loss=9.386879, lr=0.000125, time_each_step=0.27s, eta=0:18:22\n",
      "2021-08-13 17:10:09 [INFO]\t[TRAIN] Epoch=44/100, Step=35/43, loss=9.405453, lr=0.000125, time_each_step=0.25s, eta=0:18:22\n",
      "2021-08-13 17:10:10 [INFO]\t[TRAIN] Epoch=44/100, Step=37/43, loss=11.19817, lr=0.000125, time_each_step=0.26s, eta=0:18:21\n",
      "2021-08-13 17:10:10 [INFO]\t[TRAIN] Epoch=44/100, Step=39/43, loss=9.969175, lr=0.000125, time_each_step=0.25s, eta=0:18:20\n",
      "2021-08-13 17:10:10 [INFO]\t[TRAIN] Epoch=44/100, Step=41/43, loss=12.689442, lr=0.000125, time_each_step=0.23s, eta=0:18:20\n",
      "2021-08-13 17:10:11 [INFO]\t[TRAIN] Epoch=44/100, Step=43/43, loss=12.921701, lr=0.000125, time_each_step=0.23s, eta=0:18:20\n",
      "2021-08-13 17:10:11 [INFO]\t[TRAIN] Epoch 44 finished, loss=11.685683, lr=0.000125 .\n",
      "2021-08-13 17:10:17 [INFO]\t[TRAIN] Epoch=45/100, Step=2/43, loss=10.007081, lr=0.000125, time_each_step=0.48s, eta=0:16:54\n",
      "2021-08-13 17:10:17 [INFO]\t[TRAIN] Epoch=45/100, Step=4/43, loss=12.355261, lr=0.000125, time_each_step=0.48s, eta=0:16:53\n",
      "2021-08-13 17:10:18 [INFO]\t[TRAIN] Epoch=45/100, Step=6/43, loss=11.215948, lr=0.000125, time_each_step=0.49s, eta=0:16:52\n",
      "2021-08-13 17:10:18 [INFO]\t[TRAIN] Epoch=45/100, Step=8/43, loss=7.070984, lr=0.000125, time_each_step=0.5s, eta=0:16:51\n",
      "2021-08-13 17:10:19 [INFO]\t[TRAIN] Epoch=45/100, Step=10/43, loss=9.96589, lr=0.000125, time_each_step=0.52s, eta=0:16:51\n",
      "2021-08-13 17:10:20 [INFO]\t[TRAIN] Epoch=45/100, Step=12/43, loss=15.271558, lr=0.000125, time_each_step=0.53s, eta=0:16:50\n",
      "2021-08-13 17:10:20 [INFO]\t[TRAIN] Epoch=45/100, Step=14/43, loss=15.157791, lr=0.000125, time_each_step=0.53s, eta=0:16:49\n",
      "2021-08-13 17:10:21 [INFO]\t[TRAIN] Epoch=45/100, Step=16/43, loss=11.50681, lr=0.000125, time_each_step=0.55s, eta=0:16:49\n",
      "2021-08-13 17:10:22 [INFO]\t[TRAIN] Epoch=45/100, Step=18/43, loss=10.565175, lr=0.000125, time_each_step=0.56s, eta=0:16:48\n",
      "2021-08-13 17:10:22 [INFO]\t[TRAIN] Epoch=45/100, Step=20/43, loss=12.617765, lr=0.000125, time_each_step=0.57s, eta=0:16:47\n",
      "2021-08-13 17:10:23 [INFO]\t[TRAIN] Epoch=45/100, Step=22/43, loss=9.450934, lr=0.000125, time_each_step=0.31s, eta=0:16:40\n",
      "2021-08-13 17:10:23 [INFO]\t[TRAIN] Epoch=45/100, Step=24/43, loss=12.870903, lr=0.000125, time_each_step=0.31s, eta=0:16:40\n",
      "2021-08-13 17:10:24 [INFO]\t[TRAIN] Epoch=45/100, Step=26/43, loss=7.583183, lr=0.000125, time_each_step=0.29s, eta=0:16:39\n",
      "2021-08-13 17:10:24 [INFO]\t[TRAIN] Epoch=45/100, Step=28/43, loss=10.374672, lr=0.000125, time_each_step=0.28s, eta=0:16:38\n",
      "2021-08-13 17:10:25 [INFO]\t[TRAIN] Epoch=45/100, Step=30/43, loss=10.483507, lr=0.000125, time_each_step=0.27s, eta=0:16:37\n",
      "2021-08-13 17:10:25 [INFO]\t[TRAIN] Epoch=45/100, Step=32/43, loss=8.792613, lr=0.000125, time_each_step=0.26s, eta=0:16:37\n",
      "2021-08-13 17:10:25 [INFO]\t[TRAIN] Epoch=45/100, Step=34/43, loss=8.79385, lr=0.000125, time_each_step=0.25s, eta=0:16:36\n",
      "2021-08-13 17:10:26 [INFO]\t[TRAIN] Epoch=45/100, Step=36/43, loss=9.688526, lr=0.000125, time_each_step=0.24s, eta=0:16:36\n",
      "2021-08-13 17:10:26 [INFO]\t[TRAIN] Epoch=45/100, Step=38/43, loss=9.318056, lr=0.000125, time_each_step=0.24s, eta=0:16:35\n",
      "2021-08-13 17:10:27 [INFO]\t[TRAIN] Epoch=45/100, Step=40/43, loss=12.721142, lr=0.000125, time_each_step=0.22s, eta=0:16:35\n",
      "2021-08-13 17:10:27 [INFO]\t[TRAIN] Epoch=45/100, Step=42/43, loss=10.008644, lr=0.000125, time_each_step=0.22s, eta=0:16:34\n",
      "2021-08-13 17:10:27 [INFO]\t[TRAIN] Epoch 45 finished, loss=11.350432, lr=0.000125 .\n",
      "2021-08-13 17:10:32 [INFO]\t[TRAIN] Epoch=46/100, Step=1/43, loss=8.9297, lr=0.000125, time_each_step=0.45s, eta=0:16:5\n",
      "2021-08-13 17:10:33 [INFO]\t[TRAIN] Epoch=46/100, Step=3/43, loss=8.482214, lr=0.000125, time_each_step=0.46s, eta=0:16:4\n",
      "2021-08-13 17:10:33 [INFO]\t[TRAIN] Epoch=46/100, Step=5/43, loss=9.886126, lr=0.000125, time_each_step=0.45s, eta=0:16:3\n",
      "2021-08-13 17:10:34 [INFO]\t[TRAIN] Epoch=46/100, Step=7/43, loss=16.928699, lr=0.000125, time_each_step=0.47s, eta=0:16:3\n",
      "2021-08-13 17:10:35 [INFO]\t[TRAIN] Epoch=46/100, Step=9/43, loss=10.806987, lr=0.000125, time_each_step=0.48s, eta=0:16:3\n",
      "2021-08-13 17:10:35 [INFO]\t[TRAIN] Epoch=46/100, Step=11/43, loss=13.004694, lr=0.000125, time_each_step=0.5s, eta=0:16:2\n",
      "2021-08-13 17:10:36 [INFO]\t[TRAIN] Epoch=46/100, Step=13/43, loss=7.749826, lr=0.000125, time_each_step=0.51s, eta=0:16:1\n",
      "2021-08-13 17:10:36 [INFO]\t[TRAIN] Epoch=46/100, Step=15/43, loss=12.261867, lr=0.000125, time_each_step=0.5s, eta=0:16:0\n",
      "2021-08-13 17:10:37 [INFO]\t[TRAIN] Epoch=46/100, Step=17/43, loss=15.276974, lr=0.000125, time_each_step=0.51s, eta=0:16:0\n",
      "2021-08-13 17:10:38 [INFO]\t[TRAIN] Epoch=46/100, Step=19/43, loss=14.164871, lr=0.000125, time_each_step=0.53s, eta=0:15:59\n",
      "2021-08-13 17:10:38 [INFO]\t[TRAIN] Epoch=46/100, Step=21/43, loss=20.539375, lr=0.000125, time_each_step=0.3s, eta=0:15:53\n",
      "2021-08-13 17:10:39 [INFO]\t[TRAIN] Epoch=46/100, Step=23/43, loss=13.217293, lr=0.000125, time_each_step=0.29s, eta=0:15:52\n",
      "2021-08-13 17:10:39 [INFO]\t[TRAIN] Epoch=46/100, Step=25/43, loss=7.640386, lr=0.000125, time_each_step=0.29s, eta=0:15:51\n",
      "2021-08-13 17:10:39 [INFO]\t[TRAIN] Epoch=46/100, Step=27/43, loss=10.147879, lr=0.000125, time_each_step=0.27s, eta=0:15:50\n",
      "2021-08-13 17:10:40 [INFO]\t[TRAIN] Epoch=46/100, Step=29/43, loss=8.87531, lr=0.000125, time_each_step=0.24s, eta=0:15:50\n",
      "2021-08-13 17:10:40 [INFO]\t[TRAIN] Epoch=46/100, Step=31/43, loss=9.974514, lr=0.000125, time_each_step=0.24s, eta=0:15:49\n",
      "2021-08-13 17:10:40 [INFO]\t[TRAIN] Epoch=46/100, Step=33/43, loss=11.645903, lr=0.000125, time_each_step=0.23s, eta=0:15:48\n",
      "2021-08-13 17:10:41 [INFO]\t[TRAIN] Epoch=46/100, Step=35/43, loss=12.71106, lr=0.000125, time_each_step=0.23s, eta=0:15:48\n",
      "2021-08-13 17:10:41 [INFO]\t[TRAIN] Epoch=46/100, Step=37/43, loss=15.016923, lr=0.000125, time_each_step=0.22s, eta=0:15:47\n",
      "2021-08-13 17:10:42 [INFO]\t[TRAIN] Epoch=46/100, Step=39/43, loss=15.343058, lr=0.000125, time_each_step=0.22s, eta=0:15:47\n",
      "2021-08-13 17:10:42 [INFO]\t[TRAIN] Epoch=46/100, Step=41/43, loss=12.325031, lr=0.000125, time_each_step=0.2s, eta=0:15:47\n",
      "2021-08-13 17:10:43 [INFO]\t[TRAIN] Epoch=46/100, Step=43/43, loss=7.404294, lr=0.000125, time_each_step=0.2s, eta=0:15:46\n",
      "2021-08-13 17:10:43 [INFO]\t[TRAIN] Epoch 46 finished, loss=11.422697, lr=0.000125 .\n",
      "2021-08-13 17:10:48 [INFO]\t[TRAIN] Epoch=47/100, Step=2/43, loss=13.818645, lr=0.000125, time_each_step=0.46s, eta=0:14:42\n",
      "2021-08-13 17:10:49 [INFO]\t[TRAIN] Epoch=47/100, Step=4/43, loss=13.256755, lr=0.000125, time_each_step=0.48s, eta=0:14:41\n",
      "2021-08-13 17:10:50 [INFO]\t[TRAIN] Epoch=47/100, Step=6/43, loss=8.935377, lr=0.000125, time_each_step=0.51s, eta=0:14:41\n",
      "2021-08-13 17:10:51 [INFO]\t[TRAIN] Epoch=47/100, Step=8/43, loss=19.462112, lr=0.000125, time_each_step=0.53s, eta=0:14:41\n",
      "2021-08-13 17:10:51 [INFO]\t[TRAIN] Epoch=47/100, Step=10/43, loss=10.186039, lr=0.000125, time_each_step=0.54s, eta=0:14:40\n",
      "2021-08-13 17:10:52 [INFO]\t[TRAIN] Epoch=47/100, Step=12/43, loss=12.278694, lr=0.000125, time_each_step=0.55s, eta=0:14:40\n",
      "2021-08-13 17:10:53 [INFO]\t[TRAIN] Epoch=47/100, Step=14/43, loss=12.396041, lr=0.000125, time_each_step=0.56s, eta=0:14:39\n",
      "2021-08-13 17:10:53 [INFO]\t[TRAIN] Epoch=47/100, Step=16/43, loss=9.010132, lr=0.000125, time_each_step=0.55s, eta=0:14:37\n",
      "2021-08-13 17:10:53 [INFO]\t[TRAIN] Epoch=47/100, Step=18/43, loss=8.706603, lr=0.000125, time_each_step=0.55s, eta=0:14:36\n",
      "2021-08-13 17:10:54 [INFO]\t[TRAIN] Epoch=47/100, Step=20/43, loss=7.893743, lr=0.000125, time_each_step=0.55s, eta=0:14:35\n",
      "2021-08-13 17:10:54 [INFO]\t[TRAIN] Epoch=47/100, Step=22/43, loss=10.658875, lr=0.000125, time_each_step=0.29s, eta=0:14:29\n",
      "2021-08-13 17:10:55 [INFO]\t[TRAIN] Epoch=47/100, Step=24/43, loss=9.422204, lr=0.000125, time_each_step=0.27s, eta=0:14:28\n",
      "2021-08-13 17:10:55 [INFO]\t[TRAIN] Epoch=47/100, Step=26/43, loss=12.587803, lr=0.000125, time_each_step=0.25s, eta=0:14:27\n",
      "2021-08-13 17:10:55 [INFO]\t[TRAIN] Epoch=47/100, Step=28/43, loss=11.114128, lr=0.000125, time_each_step=0.22s, eta=0:14:26\n",
      "2021-08-13 17:10:55 [INFO]\t[TRAIN] Epoch=47/100, Step=30/43, loss=11.540239, lr=0.000125, time_each_step=0.21s, eta=0:14:25\n",
      "2021-08-13 17:10:56 [INFO]\t[TRAIN] Epoch=47/100, Step=32/43, loss=9.088277, lr=0.000125, time_each_step=0.19s, eta=0:14:25\n",
      "2021-08-13 17:10:56 [INFO]\t[TRAIN] Epoch=47/100, Step=34/43, loss=10.521951, lr=0.000125, time_each_step=0.18s, eta=0:14:24\n",
      "2021-08-13 17:10:56 [INFO]\t[TRAIN] Epoch=47/100, Step=36/43, loss=10.89233, lr=0.000125, time_each_step=0.17s, eta=0:14:24\n",
      "2021-08-13 17:10:57 [INFO]\t[TRAIN] Epoch=47/100, Step=38/43, loss=11.72699, lr=0.000125, time_each_step=0.17s, eta=0:14:23\n",
      "2021-08-13 17:10:57 [INFO]\t[TRAIN] Epoch=47/100, Step=40/43, loss=10.558022, lr=0.000125, time_each_step=0.18s, eta=0:14:23\n",
      "2021-08-13 17:10:58 [INFO]\t[TRAIN] Epoch=47/100, Step=42/43, loss=10.801105, lr=0.000125, time_each_step=0.18s, eta=0:14:23\n",
      "2021-08-13 17:10:58 [INFO]\t[TRAIN] Epoch 47 finished, loss=11.199298, lr=0.000125 .\n",
      "2021-08-13 17:11:03 [INFO]\t[TRAIN] Epoch=48/100, Step=1/43, loss=10.208895, lr=0.000125, time_each_step=0.41s, eta=0:14:26\n",
      "2021-08-13 17:11:03 [INFO]\t[TRAIN] Epoch=48/100, Step=3/43, loss=17.085407, lr=0.000125, time_each_step=0.43s, eta=0:14:26\n",
      "2021-08-13 17:11:04 [INFO]\t[TRAIN] Epoch=48/100, Step=5/43, loss=10.569139, lr=0.000125, time_each_step=0.46s, eta=0:14:26\n",
      "2021-08-13 17:11:05 [INFO]\t[TRAIN] Epoch=48/100, Step=7/43, loss=9.629051, lr=0.000125, time_each_step=0.46s, eta=0:14:26\n",
      "2021-08-13 17:11:05 [INFO]\t[TRAIN] Epoch=48/100, Step=9/43, loss=9.24159, lr=0.000125, time_each_step=0.47s, eta=0:14:25\n",
      "2021-08-13 17:11:06 [INFO]\t[TRAIN] Epoch=48/100, Step=11/43, loss=13.861404, lr=0.000125, time_each_step=0.5s, eta=0:14:25\n",
      "2021-08-13 17:11:07 [INFO]\t[TRAIN] Epoch=48/100, Step=13/43, loss=17.208431, lr=0.000125, time_each_step=0.51s, eta=0:14:24\n",
      "2021-08-13 17:11:07 [INFO]\t[TRAIN] Epoch=48/100, Step=15/43, loss=10.759312, lr=0.000125, time_each_step=0.53s, eta=0:14:24\n",
      "2021-08-13 17:11:08 [INFO]\t[TRAIN] Epoch=48/100, Step=17/43, loss=11.387826, lr=0.000125, time_each_step=0.53s, eta=0:14:23\n",
      "2021-08-13 17:11:08 [INFO]\t[TRAIN] Epoch=48/100, Step=19/43, loss=11.461119, lr=0.000125, time_each_step=0.53s, eta=0:14:22\n",
      "2021-08-13 17:11:09 [INFO]\t[TRAIN] Epoch=48/100, Step=21/43, loss=8.192621, lr=0.000125, time_each_step=0.32s, eta=0:14:16\n",
      "2021-08-13 17:11:10 [INFO]\t[TRAIN] Epoch=48/100, Step=23/43, loss=7.60414, lr=0.000125, time_each_step=0.32s, eta=0:14:15\n",
      "2021-08-13 17:11:10 [INFO]\t[TRAIN] Epoch=48/100, Step=25/43, loss=12.612811, lr=0.000125, time_each_step=0.3s, eta=0:14:14\n",
      "2021-08-13 17:11:10 [INFO]\t[TRAIN] Epoch=48/100, Step=27/43, loss=8.843604, lr=0.000125, time_each_step=0.28s, eta=0:14:13\n",
      "2021-08-13 17:11:11 [INFO]\t[TRAIN] Epoch=48/100, Step=29/43, loss=9.98698, lr=0.000125, time_each_step=0.28s, eta=0:14:13\n",
      "2021-08-13 17:11:11 [INFO]\t[TRAIN] Epoch=48/100, Step=31/43, loss=9.505654, lr=0.000125, time_each_step=0.25s, eta=0:14:12\n",
      "2021-08-13 17:11:12 [INFO]\t[TRAIN] Epoch=48/100, Step=33/43, loss=10.38616, lr=0.000125, time_each_step=0.24s, eta=0:14:11\n",
      "2021-08-13 17:11:12 [INFO]\t[TRAIN] Epoch=48/100, Step=35/43, loss=11.567498, lr=0.000125, time_each_step=0.22s, eta=0:14:11\n",
      "2021-08-13 17:11:12 [INFO]\t[TRAIN] Epoch=48/100, Step=37/43, loss=9.601085, lr=0.000125, time_each_step=0.22s, eta=0:14:10\n",
      "2021-08-13 17:11:13 [INFO]\t[TRAIN] Epoch=48/100, Step=39/43, loss=11.464121, lr=0.000125, time_each_step=0.22s, eta=0:14:10\n",
      "2021-08-13 17:11:13 [INFO]\t[TRAIN] Epoch=48/100, Step=41/43, loss=11.797192, lr=0.000125, time_each_step=0.2s, eta=0:14:9\n",
      "2021-08-13 17:11:14 [INFO]\t[TRAIN] Epoch=48/100, Step=43/43, loss=10.990396, lr=0.000125, time_each_step=0.19s, eta=0:14:9\n",
      "2021-08-13 17:11:14 [INFO]\t[TRAIN] Epoch 48 finished, loss=11.303546, lr=0.000125 .\n",
      "2021-08-13 17:11:18 [INFO]\t[TRAIN] Epoch=49/100, Step=2/43, loss=9.711493, lr=0.000125, time_each_step=0.38s, eta=0:14:22\n",
      "2021-08-13 17:11:18 [INFO]\t[TRAIN] Epoch=49/100, Step=4/43, loss=13.414099, lr=0.000125, time_each_step=0.39s, eta=0:14:22\n",
      "2021-08-13 17:11:19 [INFO]\t[TRAIN] Epoch=49/100, Step=6/43, loss=11.090067, lr=0.000125, time_each_step=0.4s, eta=0:14:21\n",
      "2021-08-13 17:11:19 [INFO]\t[TRAIN] Epoch=49/100, Step=8/43, loss=8.932804, lr=0.000125, time_each_step=0.42s, eta=0:14:21\n",
      "2021-08-13 17:11:20 [INFO]\t[TRAIN] Epoch=49/100, Step=10/43, loss=13.136204, lr=0.000125, time_each_step=0.43s, eta=0:14:20\n",
      "2021-08-13 17:11:21 [INFO]\t[TRAIN] Epoch=49/100, Step=12/43, loss=8.200954, lr=0.000125, time_each_step=0.44s, eta=0:14:20\n",
      "2021-08-13 17:11:21 [INFO]\t[TRAIN] Epoch=49/100, Step=14/43, loss=12.836767, lr=0.000125, time_each_step=0.44s, eta=0:14:19\n",
      "2021-08-13 17:11:22 [INFO]\t[TRAIN] Epoch=49/100, Step=16/43, loss=14.670139, lr=0.000125, time_each_step=0.44s, eta=0:14:18\n",
      "2021-08-13 17:11:22 [INFO]\t[TRAIN] Epoch=49/100, Step=18/43, loss=10.992652, lr=0.000125, time_each_step=0.45s, eta=0:14:18\n",
      "2021-08-13 17:11:23 [INFO]\t[TRAIN] Epoch=49/100, Step=20/43, loss=8.647448, lr=0.000125, time_each_step=0.46s, eta=0:14:17\n",
      "2021-08-13 17:11:23 [INFO]\t[TRAIN] Epoch=49/100, Step=22/43, loss=9.048619, lr=0.000125, time_each_step=0.27s, eta=0:14:12\n",
      "2021-08-13 17:11:24 [INFO]\t[TRAIN] Epoch=49/100, Step=24/43, loss=9.2946, lr=0.000125, time_each_step=0.28s, eta=0:14:11\n",
      "2021-08-13 17:11:24 [INFO]\t[TRAIN] Epoch=49/100, Step=26/43, loss=11.371029, lr=0.000125, time_each_step=0.27s, eta=0:14:11\n",
      "2021-08-13 17:11:24 [INFO]\t[TRAIN] Epoch=49/100, Step=28/43, loss=12.243887, lr=0.000125, time_each_step=0.25s, eta=0:14:10\n",
      "2021-08-13 17:11:25 [INFO]\t[TRAIN] Epoch=49/100, Step=30/43, loss=8.805883, lr=0.000125, time_each_step=0.24s, eta=0:14:9\n",
      "2021-08-13 17:11:25 [INFO]\t[TRAIN] Epoch=49/100, Step=32/43, loss=7.772827, lr=0.000125, time_each_step=0.24s, eta=0:14:9\n",
      "2021-08-13 17:11:26 [INFO]\t[TRAIN] Epoch=49/100, Step=34/43, loss=12.825369, lr=0.000125, time_each_step=0.24s, eta=0:14:8\n",
      "2021-08-13 17:11:26 [INFO]\t[TRAIN] Epoch=49/100, Step=36/43, loss=8.28903, lr=0.000125, time_each_step=0.23s, eta=0:14:8\n",
      "2021-08-13 17:11:27 [INFO]\t[TRAIN] Epoch=49/100, Step=38/43, loss=10.897295, lr=0.000125, time_each_step=0.22s, eta=0:14:7\n",
      "2021-08-13 17:11:27 [INFO]\t[TRAIN] Epoch=49/100, Step=40/43, loss=7.380206, lr=0.000125, time_each_step=0.21s, eta=0:14:7\n",
      "2021-08-13 17:11:27 [INFO]\t[TRAIN] Epoch=49/100, Step=42/43, loss=11.731896, lr=0.000125, time_each_step=0.21s, eta=0:14:6\n",
      "2021-08-13 17:11:28 [INFO]\t[TRAIN] Epoch 49 finished, loss=10.889365, lr=0.000125 .\n",
      "2021-08-13 17:11:33 [INFO]\t[TRAIN] Epoch=50/100, Step=1/43, loss=11.908344, lr=0.000125, time_each_step=0.48s, eta=0:12:51\n",
      "2021-08-13 17:11:34 [INFO]\t[TRAIN] Epoch=50/100, Step=3/43, loss=8.52362, lr=0.000125, time_each_step=0.49s, eta=0:12:50\n",
      "2021-08-13 17:11:34 [INFO]\t[TRAIN] Epoch=50/100, Step=5/43, loss=5.056483, lr=0.000125, time_each_step=0.5s, eta=0:12:50\n",
      "2021-08-13 17:11:35 [INFO]\t[TRAIN] Epoch=50/100, Step=7/43, loss=10.106115, lr=0.000125, time_each_step=0.52s, eta=0:12:50\n",
      "2021-08-13 17:11:36 [INFO]\t[TRAIN] Epoch=50/100, Step=9/43, loss=11.372726, lr=0.000125, time_each_step=0.53s, eta=0:12:49\n",
      "2021-08-13 17:11:36 [INFO]\t[TRAIN] Epoch=50/100, Step=11/43, loss=13.51348, lr=0.000125, time_each_step=0.53s, eta=0:12:48\n",
      "2021-08-13 17:11:37 [INFO]\t[TRAIN] Epoch=50/100, Step=13/43, loss=9.60994, lr=0.000125, time_each_step=0.54s, eta=0:12:47\n",
      "2021-08-13 17:11:38 [INFO]\t[TRAIN] Epoch=50/100, Step=15/43, loss=7.601724, lr=0.000125, time_each_step=0.55s, eta=0:12:46\n",
      "2021-08-13 17:11:38 [INFO]\t[TRAIN] Epoch=50/100, Step=17/43, loss=9.094177, lr=0.000125, time_each_step=0.58s, eta=0:12:46\n",
      "2021-08-13 17:11:39 [INFO]\t[TRAIN] Epoch=50/100, Step=19/43, loss=7.790619, lr=0.000125, time_each_step=0.57s, eta=0:12:45\n",
      "2021-08-13 17:11:39 [INFO]\t[TRAIN] Epoch=50/100, Step=21/43, loss=9.165215, lr=0.000125, time_each_step=0.3s, eta=0:12:38\n",
      "2021-08-13 17:11:40 [INFO]\t[TRAIN] Epoch=50/100, Step=23/43, loss=11.946958, lr=0.000125, time_each_step=0.29s, eta=0:12:37\n",
      "2021-08-13 17:11:40 [INFO]\t[TRAIN] Epoch=50/100, Step=25/43, loss=6.515615, lr=0.000125, time_each_step=0.28s, eta=0:12:36\n",
      "2021-08-13 17:11:40 [INFO]\t[TRAIN] Epoch=50/100, Step=27/43, loss=9.0399, lr=0.000125, time_each_step=0.26s, eta=0:12:35\n",
      "2021-08-13 17:11:41 [INFO]\t[TRAIN] Epoch=50/100, Step=29/43, loss=9.658534, lr=0.000125, time_each_step=0.24s, eta=0:12:34\n",
      "2021-08-13 17:11:41 [INFO]\t[TRAIN] Epoch=50/100, Step=31/43, loss=11.622498, lr=0.000125, time_each_step=0.24s, eta=0:12:34\n",
      "2021-08-13 17:11:42 [INFO]\t[TRAIN] Epoch=50/100, Step=33/43, loss=12.869243, lr=0.000125, time_each_step=0.24s, eta=0:12:33\n",
      "2021-08-13 17:11:42 [INFO]\t[TRAIN] Epoch=50/100, Step=35/43, loss=10.372561, lr=0.000125, time_each_step=0.22s, eta=0:12:33\n",
      "2021-08-13 17:11:43 [INFO]\t[TRAIN] Epoch=50/100, Step=37/43, loss=9.096573, lr=0.000125, time_each_step=0.21s, eta=0:12:32\n",
      "2021-08-13 17:11:43 [INFO]\t[TRAIN] Epoch=50/100, Step=39/43, loss=9.324656, lr=0.000125, time_each_step=0.21s, eta=0:12:32\n",
      "2021-08-13 17:11:43 [INFO]\t[TRAIN] Epoch=50/100, Step=41/43, loss=13.09247, lr=0.000125, time_each_step=0.2s, eta=0:12:31\n",
      "2021-08-13 17:11:44 [INFO]\t[TRAIN] Epoch=50/100, Step=43/43, loss=12.461332, lr=0.000125, time_each_step=0.21s, eta=0:12:31\n",
      "2021-08-13 17:11:44 [INFO]\t[TRAIN] Epoch 50 finished, loss=10.559318, lr=0.000125 .\n",
      "2021-08-13 17:11:44 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:06<00:00,  1.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:11:50 [INFO]\t[EVAL] Finished, Epoch=50, bbox_map=56.038571 .\n",
      "2021-08-13 17:11:52 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:11:54 [INFO]\tModel saved in output/yolov3_darknet53/epoch_50.\n",
      "2021-08-13 17:11:54 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_50, bbox_map=56.038570850773226\n",
      "2021-08-13 17:11:59 [INFO]\t[TRAIN] Epoch=51/100, Step=2/43, loss=14.801832, lr=1.2e-05, time_each_step=0.46s, eta=0:14:28\n",
      "2021-08-13 17:12:00 [INFO]\t[TRAIN] Epoch=51/100, Step=4/43, loss=12.980228, lr=1.2e-05, time_each_step=0.47s, eta=0:14:28\n",
      "2021-08-13 17:12:00 [INFO]\t[TRAIN] Epoch=51/100, Step=6/43, loss=11.311287, lr=1.2e-05, time_each_step=0.49s, eta=0:14:27\n",
      "2021-08-13 17:12:01 [INFO]\t[TRAIN] Epoch=51/100, Step=8/43, loss=11.78273, lr=1.2e-05, time_each_step=0.49s, eta=0:14:26\n",
      "2021-08-13 17:12:02 [INFO]\t[TRAIN] Epoch=51/100, Step=10/43, loss=15.523439, lr=1.2e-05, time_each_step=0.5s, eta=0:14:26\n",
      "2021-08-13 17:12:02 [INFO]\t[TRAIN] Epoch=51/100, Step=12/43, loss=9.794622, lr=1.2e-05, time_each_step=0.51s, eta=0:14:25\n",
      "2021-08-13 17:12:02 [INFO]\t[TRAIN] Epoch=51/100, Step=14/43, loss=11.990061, lr=1.2e-05, time_each_step=0.5s, eta=0:14:24\n",
      "2021-08-13 17:12:03 [INFO]\t[TRAIN] Epoch=51/100, Step=16/43, loss=12.513435, lr=1.2e-05, time_each_step=0.51s, eta=0:14:23\n",
      "2021-08-13 17:12:04 [INFO]\t[TRAIN] Epoch=51/100, Step=18/43, loss=8.368249, lr=1.2e-05, time_each_step=0.52s, eta=0:14:22\n",
      "2021-08-13 17:12:04 [INFO]\t[TRAIN] Epoch=51/100, Step=20/43, loss=11.824831, lr=1.2e-05, time_each_step=0.53s, eta=0:14:21\n",
      "2021-08-13 17:12:05 [INFO]\t[TRAIN] Epoch=51/100, Step=22/43, loss=13.442924, lr=1.2e-05, time_each_step=0.28s, eta=0:14:15\n",
      "2021-08-13 17:12:05 [INFO]\t[TRAIN] Epoch=51/100, Step=24/43, loss=7.392193, lr=1.2e-05, time_each_step=0.27s, eta=0:14:14\n",
      "2021-08-13 17:12:05 [INFO]\t[TRAIN] Epoch=51/100, Step=26/43, loss=13.35005, lr=1.2e-05, time_each_step=0.26s, eta=0:14:14\n",
      "2021-08-13 17:12:06 [INFO]\t[TRAIN] Epoch=51/100, Step=28/43, loss=12.678389, lr=1.2e-05, time_each_step=0.25s, eta=0:14:13\n",
      "2021-08-13 17:12:06 [INFO]\t[TRAIN] Epoch=51/100, Step=30/43, loss=8.898383, lr=1.2e-05, time_each_step=0.23s, eta=0:14:12\n",
      "2021-08-13 17:12:07 [INFO]\t[TRAIN] Epoch=51/100, Step=32/43, loss=11.390348, lr=1.2e-05, time_each_step=0.23s, eta=0:14:12\n",
      "2021-08-13 17:12:07 [INFO]\t[TRAIN] Epoch=51/100, Step=34/43, loss=6.988396, lr=1.2e-05, time_each_step=0.23s, eta=0:14:11\n",
      "2021-08-13 17:12:08 [INFO]\t[TRAIN] Epoch=51/100, Step=36/43, loss=13.985962, lr=1.2e-05, time_each_step=0.22s, eta=0:14:11\n",
      "2021-08-13 17:12:08 [INFO]\t[TRAIN] Epoch=51/100, Step=38/43, loss=9.873654, lr=1.2e-05, time_each_step=0.23s, eta=0:14:10\n",
      "2021-08-13 17:12:09 [INFO]\t[TRAIN] Epoch=51/100, Step=40/43, loss=12.372681, lr=1.2e-05, time_each_step=0.22s, eta=0:14:10\n",
      "2021-08-13 17:12:09 [INFO]\t[TRAIN] Epoch=51/100, Step=42/43, loss=9.401722, lr=1.2e-05, time_each_step=0.21s, eta=0:14:10\n",
      "2021-08-13 17:12:09 [INFO]\t[TRAIN] Epoch 51 finished, loss=11.026897, lr=1.3e-05 .\n",
      "2021-08-13 17:12:13 [INFO]\t[TRAIN] Epoch=52/100, Step=1/43, loss=10.084509, lr=1.2e-05, time_each_step=0.39s, eta=0:13:25\n",
      "2021-08-13 17:12:14 [INFO]\t[TRAIN] Epoch=52/100, Step=3/43, loss=10.785024, lr=1.2e-05, time_each_step=0.41s, eta=0:13:25\n",
      "2021-08-13 17:12:14 [INFO]\t[TRAIN] Epoch=52/100, Step=5/43, loss=9.0633, lr=1.2e-05, time_each_step=0.43s, eta=0:13:25\n",
      "2021-08-13 17:12:15 [INFO]\t[TRAIN] Epoch=52/100, Step=7/43, loss=11.812597, lr=1.2e-05, time_each_step=0.44s, eta=0:13:25\n",
      "2021-08-13 17:12:15 [INFO]\t[TRAIN] Epoch=52/100, Step=9/43, loss=4.918832, lr=1.2e-05, time_each_step=0.44s, eta=0:13:24\n",
      "2021-08-13 17:12:16 [INFO]\t[TRAIN] Epoch=52/100, Step=11/43, loss=9.017357, lr=1.2e-05, time_each_step=0.45s, eta=0:13:23\n",
      "2021-08-13 17:12:17 [INFO]\t[TRAIN] Epoch=52/100, Step=13/43, loss=7.963956, lr=1.2e-05, time_each_step=0.46s, eta=0:13:22\n",
      "2021-08-13 17:12:17 [INFO]\t[TRAIN] Epoch=52/100, Step=15/43, loss=6.823098, lr=1.2e-05, time_each_step=0.46s, eta=0:13:22\n",
      "2021-08-13 17:12:18 [INFO]\t[TRAIN] Epoch=52/100, Step=17/43, loss=8.664221, lr=1.2e-05, time_each_step=0.47s, eta=0:13:21\n",
      "2021-08-13 17:12:18 [INFO]\t[TRAIN] Epoch=52/100, Step=19/43, loss=9.481597, lr=1.2e-05, time_each_step=0.47s, eta=0:13:20\n",
      "2021-08-13 17:12:19 [INFO]\t[TRAIN] Epoch=52/100, Step=21/43, loss=13.405209, lr=1.2e-05, time_each_step=0.3s, eta=0:13:15\n",
      "2021-08-13 17:12:19 [INFO]\t[TRAIN] Epoch=52/100, Step=23/43, loss=5.458073, lr=1.2e-05, time_each_step=0.27s, eta=0:13:14\n",
      "2021-08-13 17:12:19 [INFO]\t[TRAIN] Epoch=52/100, Step=25/43, loss=9.492947, lr=1.2e-05, time_each_step=0.25s, eta=0:13:13\n",
      "2021-08-13 17:12:20 [INFO]\t[TRAIN] Epoch=52/100, Step=27/43, loss=6.700947, lr=1.2e-05, time_each_step=0.24s, eta=0:13:13\n",
      "2021-08-13 17:12:20 [INFO]\t[TRAIN] Epoch=52/100, Step=29/43, loss=10.534276, lr=1.2e-05, time_each_step=0.24s, eta=0:13:12\n",
      "2021-08-13 17:12:20 [INFO]\t[TRAIN] Epoch=52/100, Step=31/43, loss=9.139239, lr=1.2e-05, time_each_step=0.22s, eta=0:13:11\n",
      "2021-08-13 17:12:21 [INFO]\t[TRAIN] Epoch=52/100, Step=33/43, loss=9.44136, lr=1.2e-05, time_each_step=0.21s, eta=0:13:11\n",
      "2021-08-13 17:12:21 [INFO]\t[TRAIN] Epoch=52/100, Step=35/43, loss=7.428233, lr=1.2e-05, time_each_step=0.19s, eta=0:13:10\n",
      "2021-08-13 17:12:22 [INFO]\t[TRAIN] Epoch=52/100, Step=37/43, loss=13.31658, lr=1.2e-05, time_each_step=0.2s, eta=0:13:10\n",
      "2021-08-13 17:12:22 [INFO]\t[TRAIN] Epoch=52/100, Step=39/43, loss=7.17893, lr=1.2e-05, time_each_step=0.2s, eta=0:13:9\n",
      "2021-08-13 17:12:23 [INFO]\t[TRAIN] Epoch=52/100, Step=41/43, loss=8.959952, lr=1.2e-05, time_each_step=0.2s, eta=0:13:9\n",
      "2021-08-13 17:12:23 [INFO]\t[TRAIN] Epoch=52/100, Step=43/43, loss=12.494979, lr=1.2e-05, time_each_step=0.21s, eta=0:13:9\n",
      "2021-08-13 17:12:23 [INFO]\t[TRAIN] Epoch 52 finished, loss=9.902747, lr=1.3e-05 .\n",
      "2021-08-13 17:12:28 [INFO]\t[TRAIN] Epoch=53/100, Step=2/43, loss=7.715175, lr=1.2e-05, time_each_step=0.44s, eta=0:12:17\n",
      "2021-08-13 17:12:29 [INFO]\t[TRAIN] Epoch=53/100, Step=4/43, loss=13.324606, lr=1.2e-05, time_each_step=0.45s, eta=0:12:17\n",
      "2021-08-13 17:12:29 [INFO]\t[TRAIN] Epoch=53/100, Step=6/43, loss=10.863841, lr=1.2e-05, time_each_step=0.45s, eta=0:12:16\n",
      "2021-08-13 17:12:30 [INFO]\t[TRAIN] Epoch=53/100, Step=8/43, loss=7.042229, lr=1.2e-05, time_each_step=0.49s, eta=0:12:16\n",
      "2021-08-13 17:12:31 [INFO]\t[TRAIN] Epoch=53/100, Step=10/43, loss=11.348961, lr=1.2e-05, time_each_step=0.5s, eta=0:12:16\n",
      "2021-08-13 17:12:31 [INFO]\t[TRAIN] Epoch=53/100, Step=12/43, loss=11.086214, lr=1.2e-05, time_each_step=0.5s, eta=0:12:15\n",
      "2021-08-13 17:12:32 [INFO]\t[TRAIN] Epoch=53/100, Step=14/43, loss=8.876337, lr=1.2e-05, time_each_step=0.49s, eta=0:12:13\n",
      "2021-08-13 17:12:32 [INFO]\t[TRAIN] Epoch=53/100, Step=16/43, loss=9.893901, lr=1.2e-05, time_each_step=0.5s, eta=0:12:13\n",
      "2021-08-13 17:12:33 [INFO]\t[TRAIN] Epoch=53/100, Step=18/43, loss=11.532718, lr=1.2e-05, time_each_step=0.5s, eta=0:12:12\n",
      "2021-08-13 17:12:33 [INFO]\t[TRAIN] Epoch=53/100, Step=20/43, loss=7.916512, lr=1.2e-05, time_each_step=0.51s, eta=0:12:11\n",
      "2021-08-13 17:12:34 [INFO]\t[TRAIN] Epoch=53/100, Step=22/43, loss=7.255047, lr=1.2e-05, time_each_step=0.27s, eta=0:12:5\n",
      "2021-08-13 17:12:34 [INFO]\t[TRAIN] Epoch=53/100, Step=24/43, loss=8.981224, lr=1.2e-05, time_each_step=0.26s, eta=0:12:4\n",
      "2021-08-13 17:12:35 [INFO]\t[TRAIN] Epoch=53/100, Step=26/43, loss=7.423407, lr=1.2e-05, time_each_step=0.27s, eta=0:12:4\n",
      "2021-08-13 17:12:35 [INFO]\t[TRAIN] Epoch=53/100, Step=28/43, loss=10.869709, lr=1.2e-05, time_each_step=0.25s, eta=0:12:3\n",
      "2021-08-13 17:12:36 [INFO]\t[TRAIN] Epoch=53/100, Step=30/43, loss=12.520777, lr=1.2e-05, time_each_step=0.24s, eta=0:12:2\n",
      "2021-08-13 17:12:36 [INFO]\t[TRAIN] Epoch=53/100, Step=32/43, loss=15.646635, lr=1.2e-05, time_each_step=0.25s, eta=0:12:2\n",
      "2021-08-13 17:12:37 [INFO]\t[TRAIN] Epoch=53/100, Step=34/43, loss=8.892605, lr=1.2e-05, time_each_step=0.24s, eta=0:12:1\n",
      "2021-08-13 17:12:37 [INFO]\t[TRAIN] Epoch=53/100, Step=36/43, loss=9.295902, lr=1.2e-05, time_each_step=0.23s, eta=0:12:1\n",
      "2021-08-13 17:12:38 [INFO]\t[TRAIN] Epoch=53/100, Step=38/43, loss=13.354943, lr=1.2e-05, time_each_step=0.24s, eta=0:12:0\n",
      "2021-08-13 17:12:38 [INFO]\t[TRAIN] Epoch=53/100, Step=40/43, loss=9.34804, lr=1.2e-05, time_each_step=0.22s, eta=0:12:0\n",
      "2021-08-13 17:12:38 [INFO]\t[TRAIN] Epoch=53/100, Step=42/43, loss=15.203444, lr=1.2e-05, time_each_step=0.23s, eta=0:11:59\n",
      "2021-08-13 17:12:38 [INFO]\t[TRAIN] Epoch 53 finished, loss=10.31906, lr=1.3e-05 .\n",
      "2021-08-13 17:12:44 [INFO]\t[TRAIN] Epoch=54/100, Step=1/43, loss=6.770642, lr=1.2e-05, time_each_step=0.52s, eta=0:12:47\n",
      "2021-08-13 17:12:45 [INFO]\t[TRAIN] Epoch=54/100, Step=3/43, loss=8.601952, lr=1.2e-05, time_each_step=0.52s, eta=0:12:46\n",
      "2021-08-13 17:12:46 [INFO]\t[TRAIN] Epoch=54/100, Step=5/43, loss=21.121735, lr=1.2e-05, time_each_step=0.53s, eta=0:12:45\n",
      "2021-08-13 17:12:47 [INFO]\t[TRAIN] Epoch=54/100, Step=7/43, loss=8.500522, lr=1.2e-05, time_each_step=0.55s, eta=0:12:45\n",
      "2021-08-13 17:12:47 [INFO]\t[TRAIN] Epoch=54/100, Step=9/43, loss=10.213633, lr=1.2e-05, time_each_step=0.56s, eta=0:12:44\n",
      "2021-08-13 17:12:48 [INFO]\t[TRAIN] Epoch=54/100, Step=11/43, loss=13.870681, lr=1.2e-05, time_each_step=0.56s, eta=0:12:43\n",
      "2021-08-13 17:12:48 [INFO]\t[TRAIN] Epoch=54/100, Step=13/43, loss=6.566703, lr=1.2e-05, time_each_step=0.56s, eta=0:12:42\n",
      "2021-08-13 17:12:49 [INFO]\t[TRAIN] Epoch=54/100, Step=15/43, loss=8.447733, lr=1.2e-05, time_each_step=0.55s, eta=0:12:41\n",
      "2021-08-13 17:12:49 [INFO]\t[TRAIN] Epoch=54/100, Step=17/43, loss=9.566017, lr=1.2e-05, time_each_step=0.57s, eta=0:12:40\n",
      "2021-08-13 17:12:50 [INFO]\t[TRAIN] Epoch=54/100, Step=19/43, loss=12.863234, lr=1.2e-05, time_each_step=0.57s, eta=0:12:39\n",
      "2021-08-13 17:12:50 [INFO]\t[TRAIN] Epoch=54/100, Step=21/43, loss=7.508723, lr=1.2e-05, time_each_step=0.29s, eta=0:12:32\n",
      "2021-08-13 17:12:51 [INFO]\t[TRAIN] Epoch=54/100, Step=23/43, loss=11.84024, lr=1.2e-05, time_each_step=0.29s, eta=0:12:31\n",
      "2021-08-13 17:12:51 [INFO]\t[TRAIN] Epoch=54/100, Step=25/43, loss=16.68539, lr=1.2e-05, time_each_step=0.27s, eta=0:12:30\n",
      "2021-08-13 17:12:52 [INFO]\t[TRAIN] Epoch=54/100, Step=27/43, loss=9.71745, lr=1.2e-05, time_each_step=0.25s, eta=0:12:29\n",
      "2021-08-13 17:12:52 [INFO]\t[TRAIN] Epoch=54/100, Step=29/43, loss=12.795358, lr=1.2e-05, time_each_step=0.24s, eta=0:12:28\n",
      "2021-08-13 17:12:53 [INFO]\t[TRAIN] Epoch=54/100, Step=31/43, loss=14.429365, lr=1.2e-05, time_each_step=0.24s, eta=0:12:28\n",
      "2021-08-13 17:12:53 [INFO]\t[TRAIN] Epoch=54/100, Step=33/43, loss=8.405755, lr=1.2e-05, time_each_step=0.23s, eta=0:12:27\n",
      "2021-08-13 17:12:53 [INFO]\t[TRAIN] Epoch=54/100, Step=35/43, loss=7.808201, lr=1.2e-05, time_each_step=0.23s, eta=0:12:27\n",
      "2021-08-13 17:12:54 [INFO]\t[TRAIN] Epoch=54/100, Step=37/43, loss=8.577566, lr=1.2e-05, time_each_step=0.22s, eta=0:12:26\n",
      "2021-08-13 17:12:54 [INFO]\t[TRAIN] Epoch=54/100, Step=39/43, loss=11.951228, lr=1.2e-05, time_each_step=0.22s, eta=0:12:26\n",
      "2021-08-13 17:12:55 [INFO]\t[TRAIN] Epoch=54/100, Step=41/43, loss=10.770887, lr=1.2e-05, time_each_step=0.21s, eta=0:12:26\n",
      "2021-08-13 17:12:55 [INFO]\t[TRAIN] Epoch=54/100, Step=43/43, loss=9.377081, lr=1.2e-05, time_each_step=0.2s, eta=0:12:25\n",
      "2021-08-13 17:12:55 [INFO]\t[TRAIN] Epoch 54 finished, loss=10.827948, lr=1.3e-05 .\n",
      "2021-08-13 17:13:00 [INFO]\t[TRAIN] Epoch=55/100, Step=2/43, loss=11.276432, lr=1.2e-05, time_each_step=0.41s, eta=0:13:29\n",
      "2021-08-13 17:13:00 [INFO]\t[TRAIN] Epoch=55/100, Step=4/43, loss=12.08604, lr=1.2e-05, time_each_step=0.42s, eta=0:13:28\n",
      "2021-08-13 17:13:01 [INFO]\t[TRAIN] Epoch=55/100, Step=6/43, loss=8.849751, lr=1.2e-05, time_each_step=0.43s, eta=0:13:28\n",
      "2021-08-13 17:13:01 [INFO]\t[TRAIN] Epoch=55/100, Step=8/43, loss=12.545681, lr=1.2e-05, time_each_step=0.44s, eta=0:13:27\n",
      "2021-08-13 17:13:02 [INFO]\t[TRAIN] Epoch=55/100, Step=10/43, loss=8.908272, lr=1.2e-05, time_each_step=0.45s, eta=0:13:27\n",
      "2021-08-13 17:13:02 [INFO]\t[TRAIN] Epoch=55/100, Step=12/43, loss=8.014455, lr=1.2e-05, time_each_step=0.46s, eta=0:13:26\n",
      "2021-08-13 17:13:03 [INFO]\t[TRAIN] Epoch=55/100, Step=14/43, loss=11.415716, lr=1.2e-05, time_each_step=0.47s, eta=0:13:25\n",
      "2021-08-13 17:13:04 [INFO]\t[TRAIN] Epoch=55/100, Step=16/43, loss=14.068476, lr=1.2e-05, time_each_step=0.49s, eta=0:13:25\n",
      "2021-08-13 17:13:05 [INFO]\t[TRAIN] Epoch=55/100, Step=18/43, loss=11.615754, lr=1.2e-05, time_each_step=0.5s, eta=0:13:24\n",
      "2021-08-13 17:13:05 [INFO]\t[TRAIN] Epoch=55/100, Step=20/43, loss=9.776689, lr=1.2e-05, time_each_step=0.5s, eta=0:13:23\n",
      "2021-08-13 17:13:05 [INFO]\t[TRAIN] Epoch=55/100, Step=22/43, loss=10.354723, lr=1.2e-05, time_each_step=0.3s, eta=0:13:18\n",
      "2021-08-13 17:13:06 [INFO]\t[TRAIN] Epoch=55/100, Step=24/43, loss=16.561882, lr=1.2e-05, time_each_step=0.29s, eta=0:13:17\n",
      "2021-08-13 17:13:06 [INFO]\t[TRAIN] Epoch=55/100, Step=26/43, loss=15.784651, lr=1.2e-05, time_each_step=0.28s, eta=0:13:16\n",
      "2021-08-13 17:13:07 [INFO]\t[TRAIN] Epoch=55/100, Step=28/43, loss=9.220979, lr=1.2e-05, time_each_step=0.26s, eta=0:13:16\n",
      "2021-08-13 17:13:07 [INFO]\t[TRAIN] Epoch=55/100, Step=30/43, loss=9.365625, lr=1.2e-05, time_each_step=0.25s, eta=0:13:15\n",
      "2021-08-13 17:13:07 [INFO]\t[TRAIN] Epoch=55/100, Step=32/43, loss=9.965927, lr=1.2e-05, time_each_step=0.25s, eta=0:13:14\n",
      "2021-08-13 17:13:08 [INFO]\t[TRAIN] Epoch=55/100, Step=34/43, loss=13.369713, lr=1.2e-05, time_each_step=0.24s, eta=0:13:14\n",
      "2021-08-13 17:13:08 [INFO]\t[TRAIN] Epoch=55/100, Step=36/43, loss=7.327162, lr=1.2e-05, time_each_step=0.23s, eta=0:13:13\n",
      "2021-08-13 17:13:09 [INFO]\t[TRAIN] Epoch=55/100, Step=38/43, loss=14.706028, lr=1.2e-05, time_each_step=0.22s, eta=0:13:13\n",
      "2021-08-13 17:13:09 [INFO]\t[TRAIN] Epoch=55/100, Step=40/43, loss=9.173458, lr=1.2e-05, time_each_step=0.21s, eta=0:13:12\n",
      "2021-08-13 17:13:10 [INFO]\t[TRAIN] Epoch=55/100, Step=42/43, loss=8.899307, lr=1.2e-05, time_each_step=0.2s, eta=0:13:12\n",
      "2021-08-13 17:13:10 [INFO]\t[TRAIN] Epoch 55 finished, loss=10.665022, lr=1.3e-05 .\n",
      "2021-08-13 17:13:14 [INFO]\t[TRAIN] Epoch=56/100, Step=1/43, loss=10.489712, lr=1.2e-05, time_each_step=0.4s, eta=0:11:52\n",
      "2021-08-13 17:13:15 [INFO]\t[TRAIN] Epoch=56/100, Step=3/43, loss=7.114993, lr=1.2e-05, time_each_step=0.42s, eta=0:11:52\n",
      "2021-08-13 17:13:15 [INFO]\t[TRAIN] Epoch=56/100, Step=5/43, loss=11.791306, lr=1.2e-05, time_each_step=0.43s, eta=0:11:52\n",
      "2021-08-13 17:13:16 [INFO]\t[TRAIN] Epoch=56/100, Step=7/43, loss=9.171822, lr=1.2e-05, time_each_step=0.44s, eta=0:11:51\n",
      "2021-08-13 17:13:16 [INFO]\t[TRAIN] Epoch=56/100, Step=9/43, loss=13.033314, lr=1.2e-05, time_each_step=0.44s, eta=0:11:50\n",
      "2021-08-13 17:13:17 [INFO]\t[TRAIN] Epoch=56/100, Step=11/43, loss=6.730012, lr=1.2e-05, time_each_step=0.46s, eta=0:11:50\n",
      "2021-08-13 17:13:18 [INFO]\t[TRAIN] Epoch=56/100, Step=13/43, loss=9.543924, lr=1.2e-05, time_each_step=0.46s, eta=0:11:49\n",
      "2021-08-13 17:13:18 [INFO]\t[TRAIN] Epoch=56/100, Step=15/43, loss=8.986637, lr=1.2e-05, time_each_step=0.47s, eta=0:11:48\n",
      "2021-08-13 17:13:19 [INFO]\t[TRAIN] Epoch=56/100, Step=17/43, loss=10.284987, lr=1.2e-05, time_each_step=0.48s, eta=0:11:48\n",
      "2021-08-13 17:13:19 [INFO]\t[TRAIN] Epoch=56/100, Step=19/43, loss=12.796717, lr=1.2e-05, time_each_step=0.49s, eta=0:11:47\n",
      "2021-08-13 17:13:20 [INFO]\t[TRAIN] Epoch=56/100, Step=21/43, loss=8.979191, lr=1.2e-05, time_each_step=0.3s, eta=0:11:42\n",
      "2021-08-13 17:13:20 [INFO]\t[TRAIN] Epoch=56/100, Step=23/43, loss=10.905256, lr=1.2e-05, time_each_step=0.29s, eta=0:11:41\n",
      "2021-08-13 17:13:21 [INFO]\t[TRAIN] Epoch=56/100, Step=25/43, loss=19.116035, lr=1.2e-05, time_each_step=0.28s, eta=0:11:40\n",
      "2021-08-13 17:13:21 [INFO]\t[TRAIN] Epoch=56/100, Step=27/43, loss=9.029629, lr=1.2e-05, time_each_step=0.27s, eta=0:11:40\n",
      "2021-08-13 17:13:22 [INFO]\t[TRAIN] Epoch=56/100, Step=29/43, loss=7.915988, lr=1.2e-05, time_each_step=0.28s, eta=0:11:39\n",
      "2021-08-13 17:13:22 [INFO]\t[TRAIN] Epoch=56/100, Step=31/43, loss=11.364311, lr=1.2e-05, time_each_step=0.26s, eta=0:11:38\n",
      "2021-08-13 17:13:23 [INFO]\t[TRAIN] Epoch=56/100, Step=33/43, loss=10.902641, lr=1.2e-05, time_each_step=0.24s, eta=0:11:38\n",
      "2021-08-13 17:13:23 [INFO]\t[TRAIN] Epoch=56/100, Step=35/43, loss=8.920444, lr=1.2e-05, time_each_step=0.23s, eta=0:11:37\n",
      "2021-08-13 17:13:23 [INFO]\t[TRAIN] Epoch=56/100, Step=37/43, loss=9.814219, lr=1.2e-05, time_each_step=0.22s, eta=0:11:36\n",
      "2021-08-13 17:13:24 [INFO]\t[TRAIN] Epoch=56/100, Step=39/43, loss=12.129501, lr=1.2e-05, time_each_step=0.21s, eta=0:11:36\n",
      "2021-08-13 17:13:24 [INFO]\t[TRAIN] Epoch=56/100, Step=41/43, loss=11.404425, lr=1.2e-05, time_each_step=0.2s, eta=0:11:36\n",
      "2021-08-13 17:13:24 [INFO]\t[TRAIN] Epoch=56/100, Step=43/43, loss=10.236191, lr=1.2e-05, time_each_step=0.2s, eta=0:11:35\n",
      "2021-08-13 17:13:24 [INFO]\t[TRAIN] Epoch 56 finished, loss=10.385946, lr=1.3e-05 .\n",
      "2021-08-13 17:13:30 [INFO]\t[TRAIN] Epoch=57/100, Step=2/43, loss=7.682469, lr=1.2e-05, time_each_step=0.45s, eta=0:11:37\n",
      "2021-08-13 17:13:30 [INFO]\t[TRAIN] Epoch=57/100, Step=4/43, loss=10.433444, lr=1.2e-05, time_each_step=0.45s, eta=0:11:37\n",
      "2021-08-13 17:13:31 [INFO]\t[TRAIN] Epoch=57/100, Step=6/43, loss=8.660219, lr=1.2e-05, time_each_step=0.46s, eta=0:11:36\n",
      "2021-08-13 17:13:31 [INFO]\t[TRAIN] Epoch=57/100, Step=8/43, loss=12.580901, lr=1.2e-05, time_each_step=0.46s, eta=0:11:35\n",
      "2021-08-13 17:13:32 [INFO]\t[TRAIN] Epoch=57/100, Step=10/43, loss=8.544918, lr=1.2e-05, time_each_step=0.47s, eta=0:11:35\n",
      "2021-08-13 17:13:32 [INFO]\t[TRAIN] Epoch=57/100, Step=12/43, loss=12.893312, lr=1.2e-05, time_each_step=0.48s, eta=0:11:34\n",
      "2021-08-13 17:13:33 [INFO]\t[TRAIN] Epoch=57/100, Step=14/43, loss=11.827797, lr=1.2e-05, time_each_step=0.48s, eta=0:11:33\n",
      "2021-08-13 17:13:34 [INFO]\t[TRAIN] Epoch=57/100, Step=16/43, loss=14.677401, lr=1.2e-05, time_each_step=0.5s, eta=0:11:32\n",
      "2021-08-13 17:13:34 [INFO]\t[TRAIN] Epoch=57/100, Step=18/43, loss=10.205933, lr=1.2e-05, time_each_step=0.51s, eta=0:11:32\n",
      "2021-08-13 17:13:35 [INFO]\t[TRAIN] Epoch=57/100, Step=20/43, loss=7.093917, lr=1.2e-05, time_each_step=0.52s, eta=0:11:31\n",
      "2021-08-13 17:13:35 [INFO]\t[TRAIN] Epoch=57/100, Step=22/43, loss=7.480907, lr=1.2e-05, time_each_step=0.29s, eta=0:11:25\n",
      "2021-08-13 17:13:36 [INFO]\t[TRAIN] Epoch=57/100, Step=24/43, loss=8.456867, lr=1.2e-05, time_each_step=0.27s, eta=0:11:24\n",
      "2021-08-13 17:13:36 [INFO]\t[TRAIN] Epoch=57/100, Step=26/43, loss=11.126177, lr=1.2e-05, time_each_step=0.26s, eta=0:11:23\n",
      "2021-08-13 17:13:37 [INFO]\t[TRAIN] Epoch=57/100, Step=28/43, loss=7.933028, lr=1.2e-05, time_each_step=0.25s, eta=0:11:23\n",
      "2021-08-13 17:13:37 [INFO]\t[TRAIN] Epoch=57/100, Step=30/43, loss=8.725063, lr=1.2e-05, time_each_step=0.24s, eta=0:11:22\n",
      "2021-08-13 17:13:37 [INFO]\t[TRAIN] Epoch=57/100, Step=32/43, loss=10.843849, lr=1.2e-05, time_each_step=0.24s, eta=0:11:22\n",
      "2021-08-13 17:13:38 [INFO]\t[TRAIN] Epoch=57/100, Step=34/43, loss=7.837832, lr=1.2e-05, time_each_step=0.25s, eta=0:11:21\n",
      "2021-08-13 17:13:38 [INFO]\t[TRAIN] Epoch=57/100, Step=36/43, loss=14.349547, lr=1.2e-05, time_each_step=0.23s, eta=0:11:21\n",
      "2021-08-13 17:13:39 [INFO]\t[TRAIN] Epoch=57/100, Step=38/43, loss=10.744421, lr=1.2e-05, time_each_step=0.22s, eta=0:11:20\n",
      "2021-08-13 17:13:39 [INFO]\t[TRAIN] Epoch=57/100, Step=40/43, loss=10.423553, lr=1.2e-05, time_each_step=0.21s, eta=0:11:20\n",
      "2021-08-13 17:13:39 [INFO]\t[TRAIN] Epoch=57/100, Step=42/43, loss=12.071384, lr=1.2e-05, time_each_step=0.21s, eta=0:11:19\n",
      "2021-08-13 17:13:40 [INFO]\t[TRAIN] Epoch 57 finished, loss=10.756331, lr=1.3e-05 .\n",
      "2021-08-13 17:13:44 [INFO]\t[TRAIN] Epoch=58/100, Step=1/43, loss=7.418266, lr=1.2e-05, time_each_step=0.44s, eta=0:11:48\n",
      "2021-08-13 17:13:45 [INFO]\t[TRAIN] Epoch=58/100, Step=3/43, loss=9.744029, lr=1.2e-05, time_each_step=0.44s, eta=0:11:47\n",
      "2021-08-13 17:13:46 [INFO]\t[TRAIN] Epoch=58/100, Step=5/43, loss=11.210508, lr=1.2e-05, time_each_step=0.45s, eta=0:11:47\n",
      "2021-08-13 17:13:46 [INFO]\t[TRAIN] Epoch=58/100, Step=7/43, loss=9.332521, lr=1.2e-05, time_each_step=0.47s, eta=0:11:47\n",
      "2021-08-13 17:13:47 [INFO]\t[TRAIN] Epoch=58/100, Step=9/43, loss=9.894341, lr=1.2e-05, time_each_step=0.47s, eta=0:11:45\n",
      "2021-08-13 17:13:47 [INFO]\t[TRAIN] Epoch=58/100, Step=11/43, loss=16.567314, lr=1.2e-05, time_each_step=0.46s, eta=0:11:44\n",
      "2021-08-13 17:13:48 [INFO]\t[TRAIN] Epoch=58/100, Step=13/43, loss=9.009865, lr=1.2e-05, time_each_step=0.48s, eta=0:11:44\n",
      "2021-08-13 17:13:48 [INFO]\t[TRAIN] Epoch=58/100, Step=15/43, loss=7.620249, lr=1.2e-05, time_each_step=0.49s, eta=0:11:43\n",
      "2021-08-13 17:13:49 [INFO]\t[TRAIN] Epoch=58/100, Step=17/43, loss=9.106437, lr=1.2e-05, time_each_step=0.5s, eta=0:11:43\n",
      "2021-08-13 17:13:50 [INFO]\t[TRAIN] Epoch=58/100, Step=19/43, loss=12.091082, lr=1.2e-05, time_each_step=0.51s, eta=0:11:42\n",
      "2021-08-13 17:13:50 [INFO]\t[TRAIN] Epoch=58/100, Step=21/43, loss=9.041741, lr=1.2e-05, time_each_step=0.28s, eta=0:11:36\n",
      "2021-08-13 17:13:50 [INFO]\t[TRAIN] Epoch=58/100, Step=23/43, loss=10.638045, lr=1.2e-05, time_each_step=0.27s, eta=0:11:35\n",
      "2021-08-13 17:13:51 [INFO]\t[TRAIN] Epoch=58/100, Step=25/43, loss=19.387947, lr=1.2e-05, time_each_step=0.27s, eta=0:11:35\n",
      "2021-08-13 17:13:52 [INFO]\t[TRAIN] Epoch=58/100, Step=27/43, loss=10.689057, lr=1.2e-05, time_each_step=0.27s, eta=0:11:34\n",
      "2021-08-13 17:13:52 [INFO]\t[TRAIN] Epoch=58/100, Step=29/43, loss=9.69719, lr=1.2e-05, time_each_step=0.26s, eta=0:11:33\n",
      "2021-08-13 17:13:52 [INFO]\t[TRAIN] Epoch=58/100, Step=31/43, loss=6.646358, lr=1.2e-05, time_each_step=0.27s, eta=0:11:33\n",
      "2021-08-13 17:13:53 [INFO]\t[TRAIN] Epoch=58/100, Step=33/43, loss=15.75481, lr=1.2e-05, time_each_step=0.25s, eta=0:11:32\n",
      "2021-08-13 17:13:53 [INFO]\t[TRAIN] Epoch=58/100, Step=35/43, loss=9.753078, lr=1.2e-05, time_each_step=0.24s, eta=0:11:32\n",
      "2021-08-13 17:13:54 [INFO]\t[TRAIN] Epoch=58/100, Step=37/43, loss=7.689992, lr=1.2e-05, time_each_step=0.24s, eta=0:11:31\n",
      "2021-08-13 17:13:54 [INFO]\t[TRAIN] Epoch=58/100, Step=39/43, loss=9.041153, lr=1.2e-05, time_each_step=0.22s, eta=0:11:31\n",
      "2021-08-13 17:13:55 [INFO]\t[TRAIN] Epoch=58/100, Step=41/43, loss=13.877327, lr=1.2e-05, time_each_step=0.23s, eta=0:11:30\n",
      "2021-08-13 17:13:55 [INFO]\t[TRAIN] Epoch=58/100, Step=43/43, loss=12.313463, lr=1.2e-05, time_each_step=0.23s, eta=0:11:30\n",
      "2021-08-13 17:13:55 [INFO]\t[TRAIN] Epoch 58 finished, loss=10.546622, lr=1.3e-05 .\n",
      "2021-08-13 17:14:05 [INFO]\t[TRAIN] Epoch=59/100, Step=2/43, loss=10.088054, lr=1.2e-05, time_each_step=0.71s, eta=0:11:52\n",
      "2021-08-13 17:14:06 [INFO]\t[TRAIN] Epoch=59/100, Step=4/43, loss=8.696623, lr=1.2e-05, time_each_step=0.71s, eta=0:11:50\n",
      "2021-08-13 17:14:06 [INFO]\t[TRAIN] Epoch=59/100, Step=6/43, loss=12.322096, lr=1.2e-05, time_each_step=0.72s, eta=0:11:49\n",
      "2021-08-13 17:14:07 [INFO]\t[TRAIN] Epoch=59/100, Step=8/43, loss=10.640729, lr=1.2e-05, time_each_step=0.73s, eta=0:11:48\n",
      "2021-08-13 17:14:07 [INFO]\t[TRAIN] Epoch=59/100, Step=10/43, loss=11.510384, lr=1.2e-05, time_each_step=0.74s, eta=0:11:47\n",
      "2021-08-13 17:14:08 [INFO]\t[TRAIN] Epoch=59/100, Step=12/43, loss=8.948904, lr=1.2e-05, time_each_step=0.74s, eta=0:11:46\n",
      "2021-08-13 17:14:09 [INFO]\t[TRAIN] Epoch=59/100, Step=14/43, loss=13.190117, lr=1.2e-05, time_each_step=0.75s, eta=0:11:44\n",
      "2021-08-13 17:14:10 [INFO]\t[TRAIN] Epoch=59/100, Step=16/43, loss=10.158095, lr=1.2e-05, time_each_step=0.78s, eta=0:11:44\n",
      "2021-08-13 17:14:10 [INFO]\t[TRAIN] Epoch=59/100, Step=18/43, loss=9.386266, lr=1.2e-05, time_each_step=0.79s, eta=0:11:42\n",
      "2021-08-13 17:14:11 [INFO]\t[TRAIN] Epoch=59/100, Step=20/43, loss=6.436337, lr=1.2e-05, time_each_step=0.79s, eta=0:11:41\n",
      "2021-08-13 17:14:11 [INFO]\t[TRAIN] Epoch=59/100, Step=22/43, loss=9.037763, lr=1.2e-05, time_each_step=0.3s, eta=0:11:29\n",
      "2021-08-13 17:14:12 [INFO]\t[TRAIN] Epoch=59/100, Step=24/43, loss=9.365259, lr=1.2e-05, time_each_step=0.3s, eta=0:11:28\n",
      "2021-08-13 17:14:12 [INFO]\t[TRAIN] Epoch=59/100, Step=26/43, loss=12.335872, lr=1.2e-05, time_each_step=0.29s, eta=0:11:27\n",
      "2021-08-13 17:14:12 [INFO]\t[TRAIN] Epoch=59/100, Step=28/43, loss=10.064972, lr=1.2e-05, time_each_step=0.27s, eta=0:11:27\n",
      "2021-08-13 17:14:13 [INFO]\t[TRAIN] Epoch=59/100, Step=30/43, loss=14.261587, lr=1.2e-05, time_each_step=0.26s, eta=0:11:26\n",
      "2021-08-13 17:14:13 [INFO]\t[TRAIN] Epoch=59/100, Step=32/43, loss=17.46369, lr=1.2e-05, time_each_step=0.26s, eta=0:11:25\n",
      "2021-08-13 17:14:14 [INFO]\t[TRAIN] Epoch=59/100, Step=34/43, loss=11.97925, lr=1.2e-05, time_each_step=0.25s, eta=0:11:25\n",
      "2021-08-13 17:14:14 [INFO]\t[TRAIN] Epoch=59/100, Step=36/43, loss=8.25319, lr=1.2e-05, time_each_step=0.23s, eta=0:11:24\n",
      "2021-08-13 17:14:15 [INFO]\t[TRAIN] Epoch=59/100, Step=38/43, loss=11.204408, lr=1.2e-05, time_each_step=0.21s, eta=0:11:24\n",
      "2021-08-13 17:14:15 [INFO]\t[TRAIN] Epoch=59/100, Step=40/43, loss=7.232844, lr=1.2e-05, time_each_step=0.21s, eta=0:11:23\n",
      "2021-08-13 17:14:15 [INFO]\t[TRAIN] Epoch=59/100, Step=42/43, loss=11.148207, lr=1.2e-05, time_each_step=0.22s, eta=0:11:23\n",
      "2021-08-13 17:14:16 [INFO]\t[TRAIN] Epoch 59 finished, loss=10.877531, lr=1.3e-05 .\n",
      "2021-08-13 17:14:22 [INFO]\t[TRAIN] Epoch=60/100, Step=1/43, loss=10.758965, lr=1.2e-05, time_each_step=0.52s, eta=0:14:49\n",
      "2021-08-13 17:14:23 [INFO]\t[TRAIN] Epoch=60/100, Step=3/43, loss=9.128265, lr=1.2e-05, time_each_step=0.54s, eta=0:14:49\n",
      "2021-08-13 17:14:23 [INFO]\t[TRAIN] Epoch=60/100, Step=5/43, loss=10.975945, lr=1.2e-05, time_each_step=0.54s, eta=0:14:48\n",
      "2021-08-13 17:14:24 [INFO]\t[TRAIN] Epoch=60/100, Step=7/43, loss=9.444089, lr=1.2e-05, time_each_step=0.55s, eta=0:14:47\n",
      "2021-08-13 17:14:24 [INFO]\t[TRAIN] Epoch=60/100, Step=9/43, loss=10.273905, lr=1.2e-05, time_each_step=0.56s, eta=0:14:46\n",
      "2021-08-13 17:14:25 [INFO]\t[TRAIN] Epoch=60/100, Step=11/43, loss=10.611559, lr=1.2e-05, time_each_step=0.55s, eta=0:14:45\n",
      "2021-08-13 17:14:25 [INFO]\t[TRAIN] Epoch=60/100, Step=13/43, loss=9.829033, lr=1.2e-05, time_each_step=0.56s, eta=0:14:44\n",
      "2021-08-13 17:14:26 [INFO]\t[TRAIN] Epoch=60/100, Step=15/43, loss=11.25875, lr=1.2e-05, time_each_step=0.57s, eta=0:14:43\n",
      "2021-08-13 17:14:27 [INFO]\t[TRAIN] Epoch=60/100, Step=17/43, loss=11.639929, lr=1.2e-05, time_each_step=0.58s, eta=0:14:43\n",
      "2021-08-13 17:14:27 [INFO]\t[TRAIN] Epoch=60/100, Step=19/43, loss=11.2053, lr=1.2e-05, time_each_step=0.59s, eta=0:14:42\n",
      "2021-08-13 17:14:28 [INFO]\t[TRAIN] Epoch=60/100, Step=21/43, loss=9.393149, lr=1.2e-05, time_each_step=0.28s, eta=0:14:34\n",
      "2021-08-13 17:14:28 [INFO]\t[TRAIN] Epoch=60/100, Step=23/43, loss=9.708895, lr=1.2e-05, time_each_step=0.26s, eta=0:14:33\n",
      "2021-08-13 17:14:29 [INFO]\t[TRAIN] Epoch=60/100, Step=25/43, loss=7.772323, lr=1.2e-05, time_each_step=0.27s, eta=0:14:32\n",
      "2021-08-13 17:14:29 [INFO]\t[TRAIN] Epoch=60/100, Step=27/43, loss=7.100238, lr=1.2e-05, time_each_step=0.26s, eta=0:14:32\n",
      "2021-08-13 17:14:29 [INFO]\t[TRAIN] Epoch=60/100, Step=29/43, loss=9.182536, lr=1.2e-05, time_each_step=0.25s, eta=0:14:31\n",
      "2021-08-13 17:14:30 [INFO]\t[TRAIN] Epoch=60/100, Step=31/43, loss=11.350538, lr=1.2e-05, time_each_step=0.26s, eta=0:14:31\n",
      "2021-08-13 17:14:30 [INFO]\t[TRAIN] Epoch=60/100, Step=33/43, loss=9.113729, lr=1.2e-05, time_each_step=0.25s, eta=0:14:30\n",
      "2021-08-13 17:14:31 [INFO]\t[TRAIN] Epoch=60/100, Step=35/43, loss=10.477698, lr=1.2e-05, time_each_step=0.24s, eta=0:14:29\n",
      "2021-08-13 17:14:31 [INFO]\t[TRAIN] Epoch=60/100, Step=37/43, loss=10.110336, lr=1.2e-05, time_each_step=0.24s, eta=0:14:29\n",
      "2021-08-13 17:14:32 [INFO]\t[TRAIN] Epoch=60/100, Step=39/43, loss=7.023806, lr=1.2e-05, time_each_step=0.24s, eta=0:14:28\n",
      "2021-08-13 17:14:32 [INFO]\t[TRAIN] Epoch=60/100, Step=41/43, loss=12.507054, lr=1.2e-05, time_each_step=0.22s, eta=0:14:28\n",
      "2021-08-13 17:14:33 [INFO]\t[TRAIN] Epoch=60/100, Step=43/43, loss=12.633157, lr=1.2e-05, time_each_step=0.23s, eta=0:14:27\n",
      "2021-08-13 17:14:33 [INFO]\t[TRAIN] Epoch 60 finished, loss=10.210276, lr=1.3e-05 .\n",
      "2021-08-13 17:14:33 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:08<00:00,  1.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:14:41 [INFO]\t[EVAL] Finished, Epoch=60, bbox_map=56.676697 .\n",
      "2021-08-13 17:14:43 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:14:44 [INFO]\tModel saved in output/yolov3_darknet53/epoch_60.\n",
      "2021-08-13 17:14:44 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_60, bbox_map=56.676697348739545\n",
      "2021-08-13 17:14:49 [INFO]\t[TRAIN] Epoch=61/100, Step=2/43, loss=7.69592, lr=1.2e-05, time_each_step=0.42s, eta=0:12:10\n",
      "2021-08-13 17:14:49 [INFO]\t[TRAIN] Epoch=61/100, Step=4/43, loss=11.735308, lr=1.2e-05, time_each_step=0.43s, eta=0:12:9\n",
      "2021-08-13 17:14:50 [INFO]\t[TRAIN] Epoch=61/100, Step=6/43, loss=11.49345, lr=1.2e-05, time_each_step=0.44s, eta=0:12:9\n",
      "2021-08-13 17:14:50 [INFO]\t[TRAIN] Epoch=61/100, Step=8/43, loss=8.816006, lr=1.2e-05, time_each_step=0.43s, eta=0:12:8\n",
      "2021-08-13 17:14:51 [INFO]\t[TRAIN] Epoch=61/100, Step=10/43, loss=8.873294, lr=1.2e-05, time_each_step=0.43s, eta=0:12:7\n",
      "2021-08-13 17:14:51 [INFO]\t[TRAIN] Epoch=61/100, Step=12/43, loss=9.737337, lr=1.2e-05, time_each_step=0.44s, eta=0:12:6\n",
      "2021-08-13 17:14:52 [INFO]\t[TRAIN] Epoch=61/100, Step=14/43, loss=10.703416, lr=1.2e-05, time_each_step=0.44s, eta=0:12:5\n",
      "2021-08-13 17:14:53 [INFO]\t[TRAIN] Epoch=61/100, Step=16/43, loss=15.428153, lr=1.2e-05, time_each_step=0.46s, eta=0:12:5\n",
      "2021-08-13 17:14:53 [INFO]\t[TRAIN] Epoch=61/100, Step=18/43, loss=9.729257, lr=1.2e-05, time_each_step=0.48s, eta=0:12:4\n",
      "2021-08-13 17:14:54 [INFO]\t[TRAIN] Epoch=61/100, Step=20/43, loss=10.045473, lr=1.2e-05, time_each_step=0.48s, eta=0:12:3\n",
      "2021-08-13 17:14:55 [INFO]\t[TRAIN] Epoch=61/100, Step=22/43, loss=8.969254, lr=1.2e-05, time_each_step=0.3s, eta=0:11:59\n",
      "2021-08-13 17:14:55 [INFO]\t[TRAIN] Epoch=61/100, Step=24/43, loss=12.173134, lr=1.2e-05, time_each_step=0.29s, eta=0:11:58\n",
      "2021-08-13 17:14:56 [INFO]\t[TRAIN] Epoch=61/100, Step=26/43, loss=9.036563, lr=1.2e-05, time_each_step=0.3s, eta=0:11:58\n",
      "2021-08-13 17:14:56 [INFO]\t[TRAIN] Epoch=61/100, Step=28/43, loss=10.495491, lr=1.2e-05, time_each_step=0.3s, eta=0:11:57\n",
      "2021-08-13 17:14:56 [INFO]\t[TRAIN] Epoch=61/100, Step=30/43, loss=7.049695, lr=1.2e-05, time_each_step=0.29s, eta=0:11:56\n",
      "2021-08-13 17:14:57 [INFO]\t[TRAIN] Epoch=61/100, Step=32/43, loss=12.391326, lr=1.2e-05, time_each_step=0.29s, eta=0:11:56\n",
      "2021-08-13 17:14:57 [INFO]\t[TRAIN] Epoch=61/100, Step=34/43, loss=9.702115, lr=1.2e-05, time_each_step=0.28s, eta=0:11:55\n",
      "2021-08-13 17:14:58 [INFO]\t[TRAIN] Epoch=61/100, Step=36/43, loss=9.185782, lr=1.2e-05, time_each_step=0.26s, eta=0:11:54\n",
      "2021-08-13 17:14:58 [INFO]\t[TRAIN] Epoch=61/100, Step=38/43, loss=11.808306, lr=1.2e-05, time_each_step=0.25s, eta=0:11:54\n",
      "2021-08-13 17:14:59 [INFO]\t[TRAIN] Epoch=61/100, Step=40/43, loss=10.0828, lr=1.2e-05, time_each_step=0.24s, eta=0:11:53\n",
      "2021-08-13 17:14:59 [INFO]\t[TRAIN] Epoch=61/100, Step=42/43, loss=9.211473, lr=1.2e-05, time_each_step=0.22s, eta=0:11:53\n",
      "2021-08-13 17:14:59 [INFO]\t[TRAIN] Epoch 61 finished, loss=10.524056, lr=1.3e-05 .\n",
      "2021-08-13 17:15:03 [INFO]\t[TRAIN] Epoch=62/100, Step=1/43, loss=11.97578, lr=1.2e-05, time_each_step=0.4s, eta=0:10:37\n",
      "2021-08-13 17:15:04 [INFO]\t[TRAIN] Epoch=62/100, Step=3/43, loss=9.150957, lr=1.2e-05, time_each_step=0.4s, eta=0:10:36\n",
      "2021-08-13 17:15:04 [INFO]\t[TRAIN] Epoch=62/100, Step=5/43, loss=10.835846, lr=1.2e-05, time_each_step=0.41s, eta=0:10:35\n",
      "2021-08-13 17:15:05 [INFO]\t[TRAIN] Epoch=62/100, Step=7/43, loss=9.189455, lr=1.2e-05, time_each_step=0.42s, eta=0:10:35\n",
      "2021-08-13 17:15:05 [INFO]\t[TRAIN] Epoch=62/100, Step=9/43, loss=11.799269, lr=1.2e-05, time_each_step=0.43s, eta=0:10:34\n",
      "2021-08-13 17:15:06 [INFO]\t[TRAIN] Epoch=62/100, Step=11/43, loss=7.584395, lr=1.2e-05, time_each_step=0.45s, eta=0:10:34\n",
      "2021-08-13 17:15:07 [INFO]\t[TRAIN] Epoch=62/100, Step=13/43, loss=8.386198, lr=1.2e-05, time_each_step=0.47s, eta=0:10:34\n",
      "2021-08-13 17:15:08 [INFO]\t[TRAIN] Epoch=62/100, Step=15/43, loss=10.017378, lr=1.2e-05, time_each_step=0.5s, eta=0:10:34\n",
      "2021-08-13 17:15:09 [INFO]\t[TRAIN] Epoch=62/100, Step=17/43, loss=14.814758, lr=1.2e-05, time_each_step=0.5s, eta=0:10:33\n",
      "2021-08-13 17:15:09 [INFO]\t[TRAIN] Epoch=62/100, Step=19/43, loss=8.563208, lr=1.2e-05, time_each_step=0.51s, eta=0:10:32\n",
      "2021-08-13 17:15:10 [INFO]\t[TRAIN] Epoch=62/100, Step=21/43, loss=10.735655, lr=1.2e-05, time_each_step=0.33s, eta=0:10:27\n",
      "2021-08-13 17:15:10 [INFO]\t[TRAIN] Epoch=62/100, Step=23/43, loss=9.73218, lr=1.2e-05, time_each_step=0.32s, eta=0:10:26\n",
      "2021-08-13 17:15:11 [INFO]\t[TRAIN] Epoch=62/100, Step=25/43, loss=9.795765, lr=1.2e-05, time_each_step=0.31s, eta=0:10:25\n",
      "2021-08-13 17:15:11 [INFO]\t[TRAIN] Epoch=62/100, Step=27/43, loss=13.350344, lr=1.2e-05, time_each_step=0.3s, eta=0:10:24\n",
      "2021-08-13 17:15:11 [INFO]\t[TRAIN] Epoch=62/100, Step=29/43, loss=9.001107, lr=1.2e-05, time_each_step=0.28s, eta=0:10:24\n",
      "2021-08-13 17:15:11 [INFO]\t[TRAIN] Epoch=62/100, Step=31/43, loss=10.144445, lr=1.2e-05, time_each_step=0.26s, eta=0:10:23\n",
      "2021-08-13 17:15:12 [INFO]\t[TRAIN] Epoch=62/100, Step=33/43, loss=8.76858, lr=1.2e-05, time_each_step=0.23s, eta=0:10:22\n",
      "2021-08-13 17:15:12 [INFO]\t[TRAIN] Epoch=62/100, Step=35/43, loss=7.388552, lr=1.2e-05, time_each_step=0.22s, eta=0:10:21\n",
      "2021-08-13 17:15:13 [INFO]\t[TRAIN] Epoch=62/100, Step=37/43, loss=7.844806, lr=1.2e-05, time_each_step=0.2s, eta=0:10:21\n",
      "2021-08-13 17:15:13 [INFO]\t[TRAIN] Epoch=62/100, Step=39/43, loss=7.699063, lr=1.2e-05, time_each_step=0.19s, eta=0:10:20\n",
      "2021-08-13 17:15:13 [INFO]\t[TRAIN] Epoch=62/100, Step=41/43, loss=9.818179, lr=1.2e-05, time_each_step=0.17s, eta=0:10:20\n",
      "2021-08-13 17:15:14 [INFO]\t[TRAIN] Epoch=62/100, Step=43/43, loss=14.87747, lr=1.2e-05, time_each_step=0.18s, eta=0:10:20\n",
      "2021-08-13 17:15:14 [INFO]\t[TRAIN] Epoch 62 finished, loss=10.141521, lr=1.3e-05 .\n",
      "2021-08-13 17:15:20 [INFO]\t[TRAIN] Epoch=63/100, Step=2/43, loss=10.160587, lr=1.2e-05, time_each_step=0.47s, eta=0:9:54\n",
      "2021-08-13 17:15:20 [INFO]\t[TRAIN] Epoch=63/100, Step=4/43, loss=8.945625, lr=1.2e-05, time_each_step=0.48s, eta=0:9:54\n",
      "2021-08-13 17:15:21 [INFO]\t[TRAIN] Epoch=63/100, Step=6/43, loss=9.304405, lr=1.2e-05, time_each_step=0.49s, eta=0:9:53\n",
      "2021-08-13 17:15:22 [INFO]\t[TRAIN] Epoch=63/100, Step=8/43, loss=10.455711, lr=1.2e-05, time_each_step=0.5s, eta=0:9:53\n",
      "2021-08-13 17:15:22 [INFO]\t[TRAIN] Epoch=63/100, Step=10/43, loss=8.632898, lr=1.2e-05, time_each_step=0.52s, eta=0:9:52\n",
      "2021-08-13 17:15:23 [INFO]\t[TRAIN] Epoch=63/100, Step=12/43, loss=9.597039, lr=1.2e-05, time_each_step=0.52s, eta=0:9:51\n",
      "2021-08-13 17:15:24 [INFO]\t[TRAIN] Epoch=63/100, Step=14/43, loss=12.983347, lr=1.2e-05, time_each_step=0.54s, eta=0:9:51\n",
      "2021-08-13 17:15:24 [INFO]\t[TRAIN] Epoch=63/100, Step=16/43, loss=6.089386, lr=1.2e-05, time_each_step=0.56s, eta=0:9:50\n",
      "2021-08-13 17:15:25 [INFO]\t[TRAIN] Epoch=63/100, Step=18/43, loss=9.643381, lr=1.2e-05, time_each_step=0.58s, eta=0:9:49\n",
      "2021-08-13 17:15:25 [INFO]\t[TRAIN] Epoch=63/100, Step=20/43, loss=7.70809, lr=1.2e-05, time_each_step=0.58s, eta=0:9:48\n",
      "2021-08-13 17:15:26 [INFO]\t[TRAIN] Epoch=63/100, Step=22/43, loss=10.928328, lr=1.2e-05, time_each_step=0.28s, eta=0:9:41\n",
      "2021-08-13 17:15:26 [INFO]\t[TRAIN] Epoch=63/100, Step=24/43, loss=8.8027, lr=1.2e-05, time_each_step=0.27s, eta=0:9:40\n",
      "2021-08-13 17:15:26 [INFO]\t[TRAIN] Epoch=63/100, Step=26/43, loss=14.332754, lr=1.2e-05, time_each_step=0.27s, eta=0:9:40\n",
      "2021-08-13 17:15:27 [INFO]\t[TRAIN] Epoch=63/100, Step=28/43, loss=8.016379, lr=1.2e-05, time_each_step=0.25s, eta=0:9:39\n",
      "2021-08-13 17:15:27 [INFO]\t[TRAIN] Epoch=63/100, Step=30/43, loss=15.11215, lr=1.2e-05, time_each_step=0.25s, eta=0:9:38\n",
      "2021-08-13 17:15:28 [INFO]\t[TRAIN] Epoch=63/100, Step=32/43, loss=14.327349, lr=1.2e-05, time_each_step=0.23s, eta=0:9:38\n",
      "2021-08-13 17:15:28 [INFO]\t[TRAIN] Epoch=63/100, Step=34/43, loss=7.25207, lr=1.2e-05, time_each_step=0.23s, eta=0:9:37\n",
      "2021-08-13 17:15:29 [INFO]\t[TRAIN] Epoch=63/100, Step=36/43, loss=12.230217, lr=1.2e-05, time_each_step=0.24s, eta=0:9:37\n",
      "2021-08-13 17:15:29 [INFO]\t[TRAIN] Epoch=63/100, Step=38/43, loss=9.966917, lr=1.2e-05, time_each_step=0.22s, eta=0:9:36\n",
      "2021-08-13 17:15:30 [INFO]\t[TRAIN] Epoch=63/100, Step=40/43, loss=10.308962, lr=1.2e-05, time_each_step=0.22s, eta=0:9:36\n",
      "2021-08-13 17:15:30 [INFO]\t[TRAIN] Epoch=63/100, Step=42/43, loss=16.317047, lr=1.2e-05, time_each_step=0.22s, eta=0:9:35\n",
      "2021-08-13 17:15:30 [INFO]\t[TRAIN] Epoch 63 finished, loss=10.670898, lr=1.3e-05 .\n",
      "2021-08-13 17:15:41 [INFO]\t[TRAIN] Epoch=64/100, Step=1/43, loss=8.496651, lr=1.2e-05, time_each_step=0.76s, eta=0:11:11\n",
      "2021-08-13 17:15:42 [INFO]\t[TRAIN] Epoch=64/100, Step=3/43, loss=9.427809, lr=1.2e-05, time_each_step=0.77s, eta=0:11:10\n",
      "2021-08-13 17:15:42 [INFO]\t[TRAIN] Epoch=64/100, Step=5/43, loss=13.327803, lr=1.2e-05, time_each_step=0.79s, eta=0:11:9\n",
      "2021-08-13 17:15:43 [INFO]\t[TRAIN] Epoch=64/100, Step=7/43, loss=10.454328, lr=1.2e-05, time_each_step=0.79s, eta=0:11:7\n",
      "2021-08-13 17:15:44 [INFO]\t[TRAIN] Epoch=64/100, Step=9/43, loss=11.607939, lr=1.2e-05, time_each_step=0.8s, eta=0:11:6\n",
      "2021-08-13 17:15:44 [INFO]\t[TRAIN] Epoch=64/100, Step=11/43, loss=9.038136, lr=1.2e-05, time_each_step=0.8s, eta=0:11:5\n",
      "2021-08-13 17:15:45 [INFO]\t[TRAIN] Epoch=64/100, Step=13/43, loss=8.966221, lr=1.2e-05, time_each_step=0.79s, eta=0:11:3\n",
      "2021-08-13 17:15:45 [INFO]\t[TRAIN] Epoch=64/100, Step=15/43, loss=8.074479, lr=1.2e-05, time_each_step=0.8s, eta=0:11:1\n",
      "2021-08-13 17:15:46 [INFO]\t[TRAIN] Epoch=64/100, Step=17/43, loss=7.36433, lr=1.2e-05, time_each_step=0.81s, eta=0:11:0\n",
      "2021-08-13 17:15:46 [INFO]\t[TRAIN] Epoch=64/100, Step=19/43, loss=12.850297, lr=1.2e-05, time_each_step=0.82s, eta=0:10:59\n",
      "2021-08-13 17:15:47 [INFO]\t[TRAIN] Epoch=64/100, Step=21/43, loss=7.858061, lr=1.2e-05, time_each_step=0.29s, eta=0:10:45\n",
      "2021-08-13 17:15:47 [INFO]\t[TRAIN] Epoch=64/100, Step=23/43, loss=9.183826, lr=1.2e-05, time_each_step=0.29s, eta=0:10:45\n",
      "2021-08-13 17:15:48 [INFO]\t[TRAIN] Epoch=64/100, Step=25/43, loss=9.313981, lr=1.2e-05, time_each_step=0.28s, eta=0:10:44\n",
      "2021-08-13 17:15:48 [INFO]\t[TRAIN] Epoch=64/100, Step=27/43, loss=8.344923, lr=1.2e-05, time_each_step=0.27s, eta=0:10:43\n",
      "2021-08-13 17:15:49 [INFO]\t[TRAIN] Epoch=64/100, Step=29/43, loss=7.968297, lr=1.2e-05, time_each_step=0.26s, eta=0:10:43\n",
      "2021-08-13 17:15:49 [INFO]\t[TRAIN] Epoch=64/100, Step=31/43, loss=16.890163, lr=1.2e-05, time_each_step=0.25s, eta=0:10:42\n",
      "2021-08-13 17:15:50 [INFO]\t[TRAIN] Epoch=64/100, Step=33/43, loss=13.895597, lr=1.2e-05, time_each_step=0.26s, eta=0:10:42\n",
      "2021-08-13 17:15:50 [INFO]\t[TRAIN] Epoch=64/100, Step=35/43, loss=11.997241, lr=1.2e-05, time_each_step=0.25s, eta=0:10:41\n",
      "2021-08-13 17:15:51 [INFO]\t[TRAIN] Epoch=64/100, Step=37/43, loss=9.180205, lr=1.2e-05, time_each_step=0.24s, eta=0:10:40\n",
      "2021-08-13 17:15:51 [INFO]\t[TRAIN] Epoch=64/100, Step=39/43, loss=9.491172, lr=1.2e-05, time_each_step=0.23s, eta=0:10:40\n",
      "2021-08-13 17:15:51 [INFO]\t[TRAIN] Epoch=64/100, Step=41/43, loss=9.022387, lr=1.2e-05, time_each_step=0.22s, eta=0:10:39\n",
      "2021-08-13 17:15:52 [INFO]\t[TRAIN] Epoch=64/100, Step=43/43, loss=10.718116, lr=1.2e-05, time_each_step=0.22s, eta=0:10:39\n",
      "2021-08-13 17:15:52 [INFO]\t[TRAIN] Epoch 64 finished, loss=10.371503, lr=1.3e-05 .\n",
      "2021-08-13 17:15:59 [INFO]\t[TRAIN] Epoch=65/100, Step=2/43, loss=10.456329, lr=1.2e-05, time_each_step=0.54s, eta=0:13:44\n",
      "2021-08-13 17:15:59 [INFO]\t[TRAIN] Epoch=65/100, Step=4/43, loss=14.925741, lr=1.2e-05, time_each_step=0.54s, eta=0:13:43\n",
      "2021-08-13 17:16:00 [INFO]\t[TRAIN] Epoch=65/100, Step=6/43, loss=12.091296, lr=1.2e-05, time_each_step=0.55s, eta=0:13:42\n",
      "2021-08-13 17:16:00 [INFO]\t[TRAIN] Epoch=65/100, Step=8/43, loss=11.531209, lr=1.2e-05, time_each_step=0.56s, eta=0:13:41\n",
      "2021-08-13 17:16:01 [INFO]\t[TRAIN] Epoch=65/100, Step=10/43, loss=11.958055, lr=1.2e-05, time_each_step=0.56s, eta=0:13:40\n",
      "2021-08-13 17:16:02 [INFO]\t[TRAIN] Epoch=65/100, Step=12/43, loss=10.857656, lr=1.2e-05, time_each_step=0.57s, eta=0:13:39\n",
      "2021-08-13 17:16:02 [INFO]\t[TRAIN] Epoch=65/100, Step=14/43, loss=9.127038, lr=1.2e-05, time_each_step=0.58s, eta=0:13:39\n",
      "2021-08-13 17:16:03 [INFO]\t[TRAIN] Epoch=65/100, Step=16/43, loss=15.18911, lr=1.2e-05, time_each_step=0.6s, eta=0:13:38\n",
      "2021-08-13 17:16:04 [INFO]\t[TRAIN] Epoch=65/100, Step=18/43, loss=9.032328, lr=1.2e-05, time_each_step=0.61s, eta=0:13:37\n",
      "2021-08-13 17:16:04 [INFO]\t[TRAIN] Epoch=65/100, Step=20/43, loss=14.32991, lr=1.2e-05, time_each_step=0.63s, eta=0:13:36\n",
      "2021-08-13 17:16:05 [INFO]\t[TRAIN] Epoch=65/100, Step=22/43, loss=7.021599, lr=1.2e-05, time_each_step=0.3s, eta=0:13:28\n",
      "2021-08-13 17:16:05 [INFO]\t[TRAIN] Epoch=65/100, Step=24/43, loss=8.633841, lr=1.2e-05, time_each_step=0.3s, eta=0:13:27\n",
      "2021-08-13 17:16:05 [INFO]\t[TRAIN] Epoch=65/100, Step=26/43, loss=11.418121, lr=1.2e-05, time_each_step=0.28s, eta=0:13:27\n",
      "2021-08-13 17:16:06 [INFO]\t[TRAIN] Epoch=65/100, Step=28/43, loss=11.532476, lr=1.2e-05, time_each_step=0.27s, eta=0:13:26\n",
      "2021-08-13 17:16:06 [INFO]\t[TRAIN] Epoch=65/100, Step=30/43, loss=10.413349, lr=1.2e-05, time_each_step=0.26s, eta=0:13:25\n",
      "2021-08-13 17:16:06 [INFO]\t[TRAIN] Epoch=65/100, Step=32/43, loss=7.905907, lr=1.2e-05, time_each_step=0.24s, eta=0:13:24\n",
      "2021-08-13 17:16:07 [INFO]\t[TRAIN] Epoch=65/100, Step=34/43, loss=9.674633, lr=1.2e-05, time_each_step=0.22s, eta=0:13:24\n",
      "2021-08-13 17:16:07 [INFO]\t[TRAIN] Epoch=65/100, Step=36/43, loss=10.026615, lr=1.2e-05, time_each_step=0.2s, eta=0:13:23\n",
      "2021-08-13 17:16:07 [INFO]\t[TRAIN] Epoch=65/100, Step=38/43, loss=9.466971, lr=1.2e-05, time_each_step=0.18s, eta=0:13:23\n",
      "2021-08-13 17:16:08 [INFO]\t[TRAIN] Epoch=65/100, Step=40/43, loss=7.079588, lr=1.2e-05, time_each_step=0.17s, eta=0:13:22\n",
      "2021-08-13 17:16:08 [INFO]\t[TRAIN] Epoch=65/100, Step=42/43, loss=10.91223, lr=1.2e-05, time_each_step=0.17s, eta=0:13:22\n",
      "2021-08-13 17:16:08 [INFO]\t[TRAIN] Epoch 65 finished, loss=10.7087, lr=1.3e-05 .\n",
      "2021-08-13 17:16:13 [INFO]\t[TRAIN] Epoch=66/100, Step=1/43, loss=7.637786, lr=1.2e-05, time_each_step=0.38s, eta=0:10:29\n",
      "2021-08-13 17:16:13 [INFO]\t[TRAIN] Epoch=66/100, Step=3/43, loss=8.024155, lr=1.2e-05, time_each_step=0.39s, eta=0:10:29\n",
      "2021-08-13 17:16:14 [INFO]\t[TRAIN] Epoch=66/100, Step=5/43, loss=10.318779, lr=1.2e-05, time_each_step=0.39s, eta=0:10:28\n",
      "2021-08-13 17:16:14 [INFO]\t[TRAIN] Epoch=66/100, Step=7/43, loss=11.014853, lr=1.2e-05, time_each_step=0.41s, eta=0:10:28\n",
      "2021-08-13 17:16:15 [INFO]\t[TRAIN] Epoch=66/100, Step=9/43, loss=10.975904, lr=1.2e-05, time_each_step=0.43s, eta=0:10:28\n",
      "2021-08-13 17:16:16 [INFO]\t[TRAIN] Epoch=66/100, Step=11/43, loss=11.561442, lr=1.2e-05, time_each_step=0.45s, eta=0:10:28\n",
      "2021-08-13 17:16:16 [INFO]\t[TRAIN] Epoch=66/100, Step=13/43, loss=11.793524, lr=1.2e-05, time_each_step=0.46s, eta=0:10:27\n",
      "2021-08-13 17:16:17 [INFO]\t[TRAIN] Epoch=66/100, Step=15/43, loss=7.820197, lr=1.2e-05, time_each_step=0.49s, eta=0:10:27\n",
      "2021-08-13 17:16:18 [INFO]\t[TRAIN] Epoch=66/100, Step=17/43, loss=9.723972, lr=1.2e-05, time_each_step=0.5s, eta=0:10:26\n",
      "2021-08-13 17:16:18 [INFO]\t[TRAIN] Epoch=66/100, Step=19/43, loss=10.971838, lr=1.2e-05, time_each_step=0.5s, eta=0:10:25\n",
      "2021-08-13 17:16:19 [INFO]\t[TRAIN] Epoch=66/100, Step=21/43, loss=8.130958, lr=1.2e-05, time_each_step=0.3s, eta=0:10:20\n",
      "2021-08-13 17:16:19 [INFO]\t[TRAIN] Epoch=66/100, Step=23/43, loss=7.667622, lr=1.2e-05, time_each_step=0.29s, eta=0:10:19\n",
      "2021-08-13 17:16:20 [INFO]\t[TRAIN] Epoch=66/100, Step=25/43, loss=19.537945, lr=1.2e-05, time_each_step=0.3s, eta=0:10:19\n",
      "2021-08-13 17:16:20 [INFO]\t[TRAIN] Epoch=66/100, Step=27/43, loss=12.011097, lr=1.2e-05, time_each_step=0.28s, eta=0:10:18\n",
      "2021-08-13 17:16:20 [INFO]\t[TRAIN] Epoch=66/100, Step=29/43, loss=8.973638, lr=1.2e-05, time_each_step=0.26s, eta=0:10:17\n",
      "2021-08-13 17:16:21 [INFO]\t[TRAIN] Epoch=66/100, Step=31/43, loss=8.0665, lr=1.2e-05, time_each_step=0.26s, eta=0:10:16\n",
      "2021-08-13 17:16:21 [INFO]\t[TRAIN] Epoch=66/100, Step=33/43, loss=6.580414, lr=1.2e-05, time_each_step=0.26s, eta=0:10:16\n",
      "2021-08-13 17:16:22 [INFO]\t[TRAIN] Epoch=66/100, Step=35/43, loss=16.13735, lr=1.2e-05, time_each_step=0.23s, eta=0:10:15\n",
      "2021-08-13 17:16:22 [INFO]\t[TRAIN] Epoch=66/100, Step=37/43, loss=11.188009, lr=1.2e-05, time_each_step=0.23s, eta=0:10:15\n",
      "2021-08-13 17:16:23 [INFO]\t[TRAIN] Epoch=66/100, Step=39/43, loss=11.884502, lr=1.2e-05, time_each_step=0.22s, eta=0:10:14\n",
      "2021-08-13 17:16:23 [INFO]\t[TRAIN] Epoch=66/100, Step=41/43, loss=10.256821, lr=1.2e-05, time_each_step=0.21s, eta=0:10:14\n",
      "2021-08-13 17:16:23 [INFO]\t[TRAIN] Epoch=66/100, Step=43/43, loss=10.923888, lr=1.2e-05, time_each_step=0.21s, eta=0:10:13\n",
      "2021-08-13 17:16:23 [INFO]\t[TRAIN] Epoch 66 finished, loss=10.533563, lr=1.3e-05 .\n",
      "2021-08-13 17:16:29 [INFO]\t[TRAIN] Epoch=67/100, Step=2/43, loss=9.755856, lr=1.2e-05, time_each_step=0.45s, eta=0:9:17\n",
      "2021-08-13 17:16:30 [INFO]\t[TRAIN] Epoch=67/100, Step=4/43, loss=13.033216, lr=1.2e-05, time_each_step=0.48s, eta=0:9:17\n",
      "2021-08-13 17:16:30 [INFO]\t[TRAIN] Epoch=67/100, Step=6/43, loss=7.796524, lr=1.2e-05, time_each_step=0.5s, eta=0:9:17\n",
      "2021-08-13 17:16:31 [INFO]\t[TRAIN] Epoch=67/100, Step=8/43, loss=12.726666, lr=1.2e-05, time_each_step=0.49s, eta=0:9:16\n",
      "2021-08-13 17:16:31 [INFO]\t[TRAIN] Epoch=67/100, Step=10/43, loss=9.992929, lr=1.2e-05, time_each_step=0.5s, eta=0:9:15\n",
      "2021-08-13 17:16:32 [INFO]\t[TRAIN] Epoch=67/100, Step=12/43, loss=7.502642, lr=1.2e-05, time_each_step=0.51s, eta=0:9:15\n",
      "2021-08-13 17:16:32 [INFO]\t[TRAIN] Epoch=67/100, Step=14/43, loss=11.682318, lr=1.2e-05, time_each_step=0.51s, eta=0:9:14\n",
      "2021-08-13 17:16:33 [INFO]\t[TRAIN] Epoch=67/100, Step=16/43, loss=9.29994, lr=1.2e-05, time_each_step=0.53s, eta=0:9:13\n",
      "2021-08-13 17:16:34 [INFO]\t[TRAIN] Epoch=67/100, Step=18/43, loss=11.919891, lr=1.2e-05, time_each_step=0.55s, eta=0:9:13\n",
      "2021-08-13 17:16:34 [INFO]\t[TRAIN] Epoch=67/100, Step=20/43, loss=10.982738, lr=1.2e-05, time_each_step=0.55s, eta=0:9:12\n",
      "2021-08-13 17:16:35 [INFO]\t[TRAIN] Epoch=67/100, Step=22/43, loss=11.389491, lr=1.2e-05, time_each_step=0.31s, eta=0:9:5\n",
      "2021-08-13 17:16:35 [INFO]\t[TRAIN] Epoch=67/100, Step=24/43, loss=7.656744, lr=1.2e-05, time_each_step=0.28s, eta=0:9:4\n",
      "2021-08-13 17:16:35 [INFO]\t[TRAIN] Epoch=67/100, Step=26/43, loss=18.445103, lr=1.2e-05, time_each_step=0.26s, eta=0:9:3\n",
      "2021-08-13 17:16:36 [INFO]\t[TRAIN] Epoch=67/100, Step=28/43, loss=8.084442, lr=1.2e-05, time_each_step=0.24s, eta=0:9:3\n",
      "2021-08-13 17:16:36 [INFO]\t[TRAIN] Epoch=67/100, Step=30/43, loss=17.381783, lr=1.2e-05, time_each_step=0.23s, eta=0:9:2\n",
      "2021-08-13 17:16:36 [INFO]\t[TRAIN] Epoch=67/100, Step=32/43, loss=11.619249, lr=1.2e-05, time_each_step=0.23s, eta=0:9:1\n",
      "2021-08-13 17:16:37 [INFO]\t[TRAIN] Epoch=67/100, Step=34/43, loss=8.790782, lr=1.2e-05, time_each_step=0.22s, eta=0:9:1\n",
      "2021-08-13 17:16:37 [INFO]\t[TRAIN] Epoch=67/100, Step=36/43, loss=6.713193, lr=1.2e-05, time_each_step=0.2s, eta=0:9:0\n",
      "2021-08-13 17:16:38 [INFO]\t[TRAIN] Epoch=67/100, Step=38/43, loss=10.113272, lr=1.2e-05, time_each_step=0.18s, eta=0:9:0\n",
      "2021-08-13 17:16:38 [INFO]\t[TRAIN] Epoch=67/100, Step=40/43, loss=8.017392, lr=1.2e-05, time_each_step=0.19s, eta=0:8:59\n",
      "2021-08-13 17:16:39 [INFO]\t[TRAIN] Epoch=67/100, Step=42/43, loss=8.407512, lr=1.2e-05, time_each_step=0.19s, eta=0:8:59\n",
      "2021-08-13 17:16:39 [INFO]\t[TRAIN] Epoch 67 finished, loss=10.81785, lr=1.3e-05 .\n",
      "2021-08-13 17:16:45 [INFO]\t[TRAIN] Epoch=68/100, Step=1/43, loss=17.016565, lr=1.2e-05, time_each_step=0.47s, eta=0:9:23\n",
      "2021-08-13 17:16:45 [INFO]\t[TRAIN] Epoch=68/100, Step=3/43, loss=11.270267, lr=1.2e-05, time_each_step=0.48s, eta=0:9:23\n",
      "2021-08-13 17:16:46 [INFO]\t[TRAIN] Epoch=68/100, Step=5/43, loss=10.695326, lr=1.2e-05, time_each_step=0.52s, eta=0:9:24\n",
      "2021-08-13 17:16:47 [INFO]\t[TRAIN] Epoch=68/100, Step=7/43, loss=11.436239, lr=1.2e-05, time_each_step=0.54s, eta=0:9:23\n",
      "2021-08-13 17:16:47 [INFO]\t[TRAIN] Epoch=68/100, Step=9/43, loss=12.87871, lr=1.2e-05, time_each_step=0.55s, eta=0:9:22\n",
      "2021-08-13 17:16:48 [INFO]\t[TRAIN] Epoch=68/100, Step=11/43, loss=8.564298, lr=1.2e-05, time_each_step=0.55s, eta=0:9:22\n",
      "2021-08-13 17:16:48 [INFO]\t[TRAIN] Epoch=68/100, Step=13/43, loss=9.836917, lr=1.2e-05, time_each_step=0.56s, eta=0:9:20\n",
      "2021-08-13 17:16:49 [INFO]\t[TRAIN] Epoch=68/100, Step=15/43, loss=9.405044, lr=1.2e-05, time_each_step=0.55s, eta=0:9:19\n",
      "2021-08-13 17:16:49 [INFO]\t[TRAIN] Epoch=68/100, Step=17/43, loss=12.665921, lr=1.2e-05, time_each_step=0.55s, eta=0:9:18\n",
      "2021-08-13 17:16:50 [INFO]\t[TRAIN] Epoch=68/100, Step=19/43, loss=9.502529, lr=1.2e-05, time_each_step=0.55s, eta=0:9:17\n",
      "2021-08-13 17:16:50 [INFO]\t[TRAIN] Epoch=68/100, Step=21/43, loss=6.801841, lr=1.2e-05, time_each_step=0.28s, eta=0:9:10\n",
      "2021-08-13 17:16:51 [INFO]\t[TRAIN] Epoch=68/100, Step=23/43, loss=10.911337, lr=1.2e-05, time_each_step=0.28s, eta=0:9:9\n",
      "2021-08-13 17:16:52 [INFO]\t[TRAIN] Epoch=68/100, Step=25/43, loss=8.601663, lr=1.2e-05, time_each_step=0.27s, eta=0:9:9\n",
      "2021-08-13 17:16:52 [INFO]\t[TRAIN] Epoch=68/100, Step=27/43, loss=10.028485, lr=1.2e-05, time_each_step=0.25s, eta=0:9:8\n",
      "2021-08-13 17:16:52 [INFO]\t[TRAIN] Epoch=68/100, Step=29/43, loss=12.254953, lr=1.2e-05, time_each_step=0.25s, eta=0:9:7\n",
      "2021-08-13 17:16:53 [INFO]\t[TRAIN] Epoch=68/100, Step=31/43, loss=6.583887, lr=1.2e-05, time_each_step=0.25s, eta=0:9:7\n",
      "2021-08-13 17:16:53 [INFO]\t[TRAIN] Epoch=68/100, Step=33/43, loss=8.639153, lr=1.2e-05, time_each_step=0.25s, eta=0:9:6\n",
      "2021-08-13 17:16:54 [INFO]\t[TRAIN] Epoch=68/100, Step=35/43, loss=9.04715, lr=1.2e-05, time_each_step=0.26s, eta=0:9:6\n",
      "2021-08-13 17:16:54 [INFO]\t[TRAIN] Epoch=68/100, Step=37/43, loss=7.733372, lr=1.2e-05, time_each_step=0.24s, eta=0:9:5\n",
      "2021-08-13 17:16:55 [INFO]\t[TRAIN] Epoch=68/100, Step=39/43, loss=7.727166, lr=1.2e-05, time_each_step=0.25s, eta=0:9:5\n",
      "2021-08-13 17:16:55 [INFO]\t[TRAIN] Epoch=68/100, Step=41/43, loss=11.214748, lr=1.2e-05, time_each_step=0.24s, eta=0:9:4\n",
      "2021-08-13 17:16:55 [INFO]\t[TRAIN] Epoch=68/100, Step=43/43, loss=11.352186, lr=1.2e-05, time_each_step=0.22s, eta=0:9:4\n",
      "2021-08-13 17:16:55 [INFO]\t[TRAIN] Epoch 68 finished, loss=10.239556, lr=1.3e-05 .\n",
      "2021-08-13 17:17:00 [INFO]\t[TRAIN] Epoch=69/100, Step=2/43, loss=10.156012, lr=1.2e-05, time_each_step=0.4s, eta=0:9:30\n",
      "2021-08-13 17:17:00 [INFO]\t[TRAIN] Epoch=69/100, Step=4/43, loss=8.66794, lr=1.2e-05, time_each_step=0.41s, eta=0:9:30\n",
      "2021-08-13 17:17:01 [INFO]\t[TRAIN] Epoch=69/100, Step=6/43, loss=7.385464, lr=1.2e-05, time_each_step=0.42s, eta=0:9:29\n",
      "2021-08-13 17:17:02 [INFO]\t[TRAIN] Epoch=69/100, Step=8/43, loss=7.38985, lr=1.2e-05, time_each_step=0.43s, eta=0:9:29\n",
      "2021-08-13 17:17:02 [INFO]\t[TRAIN] Epoch=69/100, Step=10/43, loss=8.409909, lr=1.2e-05, time_each_step=0.45s, eta=0:9:29\n",
      "2021-08-13 17:17:03 [INFO]\t[TRAIN] Epoch=69/100, Step=12/43, loss=10.275504, lr=1.2e-05, time_each_step=0.45s, eta=0:9:28\n",
      "2021-08-13 17:17:03 [INFO]\t[TRAIN] Epoch=69/100, Step=14/43, loss=9.476043, lr=1.2e-05, time_each_step=0.47s, eta=0:9:27\n",
      "2021-08-13 17:17:04 [INFO]\t[TRAIN] Epoch=69/100, Step=16/43, loss=11.277039, lr=1.2e-05, time_each_step=0.47s, eta=0:9:27\n",
      "2021-08-13 17:17:05 [INFO]\t[TRAIN] Epoch=69/100, Step=18/43, loss=14.4467, lr=1.2e-05, time_each_step=0.48s, eta=0:9:26\n",
      "2021-08-13 17:17:05 [INFO]\t[TRAIN] Epoch=69/100, Step=20/43, loss=12.871447, lr=1.2e-05, time_each_step=0.49s, eta=0:9:25\n",
      "2021-08-13 17:17:06 [INFO]\t[TRAIN] Epoch=69/100, Step=22/43, loss=10.753803, lr=1.2e-05, time_each_step=0.31s, eta=0:9:20\n",
      "2021-08-13 17:17:06 [INFO]\t[TRAIN] Epoch=69/100, Step=24/43, loss=7.980417, lr=1.2e-05, time_each_step=0.3s, eta=0:9:20\n",
      "2021-08-13 17:17:07 [INFO]\t[TRAIN] Epoch=69/100, Step=26/43, loss=14.624339, lr=1.2e-05, time_each_step=0.29s, eta=0:9:19\n",
      "2021-08-13 17:17:07 [INFO]\t[TRAIN] Epoch=69/100, Step=28/43, loss=9.803949, lr=1.2e-05, time_each_step=0.27s, eta=0:9:18\n",
      "2021-08-13 17:17:07 [INFO]\t[TRAIN] Epoch=69/100, Step=30/43, loss=8.749099, lr=1.2e-05, time_each_step=0.26s, eta=0:9:17\n",
      "2021-08-13 17:17:08 [INFO]\t[TRAIN] Epoch=69/100, Step=32/43, loss=11.166458, lr=1.2e-05, time_each_step=0.26s, eta=0:9:17\n",
      "2021-08-13 17:17:08 [INFO]\t[TRAIN] Epoch=69/100, Step=34/43, loss=10.31088, lr=1.2e-05, time_each_step=0.25s, eta=0:9:16\n",
      "2021-08-13 17:17:09 [INFO]\t[TRAIN] Epoch=69/100, Step=36/43, loss=7.43754, lr=1.2e-05, time_each_step=0.24s, eta=0:9:16\n",
      "2021-08-13 17:17:09 [INFO]\t[TRAIN] Epoch=69/100, Step=38/43, loss=14.483525, lr=1.2e-05, time_each_step=0.22s, eta=0:9:15\n",
      "2021-08-13 17:17:10 [INFO]\t[TRAIN] Epoch=69/100, Step=40/43, loss=13.146658, lr=1.2e-05, time_each_step=0.23s, eta=0:9:15\n",
      "2021-08-13 17:17:10 [INFO]\t[TRAIN] Epoch=69/100, Step=42/43, loss=6.862926, lr=1.2e-05, time_each_step=0.23s, eta=0:9:14\n",
      "2021-08-13 17:17:11 [INFO]\t[TRAIN] Epoch 69 finished, loss=9.766765, lr=1.3e-05 .\n",
      "2021-08-13 17:17:16 [INFO]\t[TRAIN] Epoch=70/100, Step=1/43, loss=9.695038, lr=1.2e-05, time_each_step=0.5s, eta=0:8:43\n",
      "2021-08-13 17:17:17 [INFO]\t[TRAIN] Epoch=70/100, Step=3/43, loss=8.91938, lr=1.2e-05, time_each_step=0.5s, eta=0:8:42\n",
      "2021-08-13 17:17:17 [INFO]\t[TRAIN] Epoch=70/100, Step=5/43, loss=11.973713, lr=1.2e-05, time_each_step=0.51s, eta=0:8:41\n",
      "2021-08-13 17:17:18 [INFO]\t[TRAIN] Epoch=70/100, Step=7/43, loss=10.602291, lr=1.2e-05, time_each_step=0.52s, eta=0:8:40\n",
      "2021-08-13 17:17:19 [INFO]\t[TRAIN] Epoch=70/100, Step=9/43, loss=11.677604, lr=1.2e-05, time_each_step=0.53s, eta=0:8:40\n",
      "2021-08-13 17:17:19 [INFO]\t[TRAIN] Epoch=70/100, Step=11/43, loss=13.615547, lr=1.2e-05, time_each_step=0.53s, eta=0:8:39\n",
      "2021-08-13 17:17:20 [INFO]\t[TRAIN] Epoch=70/100, Step=13/43, loss=10.10017, lr=1.2e-05, time_each_step=0.54s, eta=0:8:38\n",
      "2021-08-13 17:17:20 [INFO]\t[TRAIN] Epoch=70/100, Step=15/43, loss=10.473652, lr=1.2e-05, time_each_step=0.55s, eta=0:8:37\n",
      "2021-08-13 17:17:21 [INFO]\t[TRAIN] Epoch=70/100, Step=17/43, loss=11.569731, lr=1.2e-05, time_each_step=0.55s, eta=0:8:36\n",
      "2021-08-13 17:17:21 [INFO]\t[TRAIN] Epoch=70/100, Step=19/43, loss=9.263309, lr=1.2e-05, time_each_step=0.55s, eta=0:8:35\n",
      "2021-08-13 17:17:22 [INFO]\t[TRAIN] Epoch=70/100, Step=21/43, loss=9.6476, lr=1.2e-05, time_each_step=0.27s, eta=0:8:28\n",
      "2021-08-13 17:17:22 [INFO]\t[TRAIN] Epoch=70/100, Step=23/43, loss=9.004431, lr=1.2e-05, time_each_step=0.27s, eta=0:8:27\n",
      "2021-08-13 17:17:23 [INFO]\t[TRAIN] Epoch=70/100, Step=25/43, loss=9.201895, lr=1.2e-05, time_each_step=0.27s, eta=0:8:27\n",
      "2021-08-13 17:17:23 [INFO]\t[TRAIN] Epoch=70/100, Step=27/43, loss=8.240559, lr=1.2e-05, time_each_step=0.26s, eta=0:8:26\n",
      "2021-08-13 17:17:23 [INFO]\t[TRAIN] Epoch=70/100, Step=29/43, loss=7.830675, lr=1.2e-05, time_each_step=0.24s, eta=0:8:25\n",
      "2021-08-13 17:17:24 [INFO]\t[TRAIN] Epoch=70/100, Step=31/43, loss=7.820754, lr=1.2e-05, time_each_step=0.23s, eta=0:8:25\n",
      "2021-08-13 17:17:24 [INFO]\t[TRAIN] Epoch=70/100, Step=33/43, loss=10.966647, lr=1.2e-05, time_each_step=0.22s, eta=0:8:24\n",
      "2021-08-13 17:17:24 [INFO]\t[TRAIN] Epoch=70/100, Step=35/43, loss=10.712475, lr=1.2e-05, time_each_step=0.21s, eta=0:8:23\n",
      "2021-08-13 17:17:25 [INFO]\t[TRAIN] Epoch=70/100, Step=37/43, loss=7.167218, lr=1.2e-05, time_each_step=0.2s, eta=0:8:23\n",
      "2021-08-13 17:17:25 [INFO]\t[TRAIN] Epoch=70/100, Step=39/43, loss=8.886757, lr=1.2e-05, time_each_step=0.21s, eta=0:8:23\n",
      "2021-08-13 17:17:26 [INFO]\t[TRAIN] Epoch=70/100, Step=41/43, loss=11.736097, lr=1.2e-05, time_each_step=0.2s, eta=0:8:22\n",
      "2021-08-13 17:17:26 [INFO]\t[TRAIN] Epoch=70/100, Step=43/43, loss=11.512471, lr=1.2e-05, time_each_step=0.2s, eta=0:8:22\n",
      "2021-08-13 17:17:26 [INFO]\t[TRAIN] Epoch 70 finished, loss=10.121106, lr=1.3e-05 .\n",
      "2021-08-13 17:17:26 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:11<00:00,  1.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:17:38 [INFO]\t[EVAL] Finished, Epoch=70, bbox_map=59.704086 .\n",
      "2021-08-13 17:17:39 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:17:41 [INFO]\tModel saved in output/yolov3_darknet53/epoch_70.\n",
      "2021-08-13 17:17:41 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_70, bbox_map=59.704085521033356\n",
      "2021-08-13 17:17:45 [INFO]\t[TRAIN] Epoch=71/100, Step=2/43, loss=9.070077, lr=1.2e-05, time_each_step=0.39s, eta=0:8:31\n",
      "2021-08-13 17:17:46 [INFO]\t[TRAIN] Epoch=71/100, Step=4/43, loss=17.20084, lr=1.2e-05, time_each_step=0.4s, eta=0:8:30\n",
      "2021-08-13 17:17:46 [INFO]\t[TRAIN] Epoch=71/100, Step=6/43, loss=9.568113, lr=1.2e-05, time_each_step=0.4s, eta=0:8:30\n",
      "2021-08-13 17:17:46 [INFO]\t[TRAIN] Epoch=71/100, Step=8/43, loss=14.683588, lr=1.2e-05, time_each_step=0.41s, eta=0:8:29\n",
      "2021-08-13 17:17:47 [INFO]\t[TRAIN] Epoch=71/100, Step=10/43, loss=10.295393, lr=1.2e-05, time_each_step=0.43s, eta=0:8:29\n",
      "2021-08-13 17:17:48 [INFO]\t[TRAIN] Epoch=71/100, Step=12/43, loss=7.367618, lr=1.2e-05, time_each_step=0.45s, eta=0:8:29\n",
      "2021-08-13 17:17:48 [INFO]\t[TRAIN] Epoch=71/100, Step=14/43, loss=11.678659, lr=1.2e-05, time_each_step=0.46s, eta=0:8:28\n",
      "2021-08-13 17:17:49 [INFO]\t[TRAIN] Epoch=71/100, Step=16/43, loss=10.810177, lr=1.2e-05, time_each_step=0.47s, eta=0:8:27\n",
      "2021-08-13 17:17:50 [INFO]\t[TRAIN] Epoch=71/100, Step=18/43, loss=11.501464, lr=1.2e-05, time_each_step=0.48s, eta=0:8:27\n",
      "2021-08-13 17:17:50 [INFO]\t[TRAIN] Epoch=71/100, Step=20/43, loss=10.185814, lr=1.2e-05, time_each_step=0.48s, eta=0:8:26\n",
      "2021-08-13 17:17:51 [INFO]\t[TRAIN] Epoch=71/100, Step=22/43, loss=15.553685, lr=1.2e-05, time_each_step=0.29s, eta=0:8:21\n",
      "2021-08-13 17:17:51 [INFO]\t[TRAIN] Epoch=71/100, Step=24/43, loss=8.516947, lr=1.2e-05, time_each_step=0.28s, eta=0:8:20\n",
      "2021-08-13 17:17:51 [INFO]\t[TRAIN] Epoch=71/100, Step=26/43, loss=10.507534, lr=1.2e-05, time_each_step=0.27s, eta=0:8:19\n",
      "2021-08-13 17:17:52 [INFO]\t[TRAIN] Epoch=71/100, Step=28/43, loss=13.943102, lr=1.2e-05, time_each_step=0.28s, eta=0:8:19\n",
      "2021-08-13 17:17:52 [INFO]\t[TRAIN] Epoch=71/100, Step=30/43, loss=15.992914, lr=1.2e-05, time_each_step=0.26s, eta=0:8:18\n",
      "2021-08-13 17:17:53 [INFO]\t[TRAIN] Epoch=71/100, Step=32/43, loss=8.404118, lr=1.2e-05, time_each_step=0.25s, eta=0:8:17\n",
      "2021-08-13 17:17:53 [INFO]\t[TRAIN] Epoch=71/100, Step=34/43, loss=12.463985, lr=1.2e-05, time_each_step=0.23s, eta=0:8:17\n",
      "2021-08-13 17:17:53 [INFO]\t[TRAIN] Epoch=71/100, Step=36/43, loss=8.996551, lr=1.2e-05, time_each_step=0.21s, eta=0:8:16\n",
      "2021-08-13 17:17:54 [INFO]\t[TRAIN] Epoch=71/100, Step=38/43, loss=9.316652, lr=1.2e-05, time_each_step=0.2s, eta=0:8:16\n",
      "2021-08-13 17:17:54 [INFO]\t[TRAIN] Epoch=71/100, Step=40/43, loss=9.879491, lr=1.2e-05, time_each_step=0.19s, eta=0:8:15\n",
      "2021-08-13 17:17:55 [INFO]\t[TRAIN] Epoch=71/100, Step=42/43, loss=11.987179, lr=1.2e-05, time_each_step=0.2s, eta=0:8:15\n",
      "2021-08-13 17:17:55 [INFO]\t[TRAIN] Epoch 71 finished, loss=10.84938, lr=1.3e-05 .\n",
      "2021-08-13 17:18:03 [INFO]\t[TRAIN] Epoch=72/100, Step=1/43, loss=9.667508, lr=1.2e-05, time_each_step=0.59s, eta=0:7:42\n",
      "2021-08-13 17:18:04 [INFO]\t[TRAIN] Epoch=72/100, Step=3/43, loss=8.415845, lr=1.2e-05, time_each_step=0.61s, eta=0:7:42\n",
      "2021-08-13 17:18:04 [INFO]\t[TRAIN] Epoch=72/100, Step=5/43, loss=11.09911, lr=1.2e-05, time_each_step=0.61s, eta=0:7:41\n",
      "2021-08-13 17:18:05 [INFO]\t[TRAIN] Epoch=72/100, Step=7/43, loss=8.138638, lr=1.2e-05, time_each_step=0.63s, eta=0:7:40\n",
      "2021-08-13 17:18:05 [INFO]\t[TRAIN] Epoch=72/100, Step=9/43, loss=10.207981, lr=1.2e-05, time_each_step=0.63s, eta=0:7:39\n",
      "2021-08-13 17:18:06 [INFO]\t[TRAIN] Epoch=72/100, Step=11/43, loss=14.297028, lr=1.2e-05, time_each_step=0.64s, eta=0:7:38\n",
      "2021-08-13 17:18:07 [INFO]\t[TRAIN] Epoch=72/100, Step=13/43, loss=14.092303, lr=1.2e-05, time_each_step=0.66s, eta=0:7:37\n",
      "2021-08-13 17:18:07 [INFO]\t[TRAIN] Epoch=72/100, Step=15/43, loss=11.434811, lr=1.2e-05, time_each_step=0.68s, eta=0:7:36\n",
      "2021-08-13 17:18:08 [INFO]\t[TRAIN] Epoch=72/100, Step=17/43, loss=10.878487, lr=1.2e-05, time_each_step=0.7s, eta=0:7:35\n",
      "2021-08-13 17:18:09 [INFO]\t[TRAIN] Epoch=72/100, Step=19/43, loss=9.911379, lr=1.2e-05, time_each_step=0.7s, eta=0:7:34\n",
      "2021-08-13 17:18:09 [INFO]\t[TRAIN] Epoch=72/100, Step=21/43, loss=16.471342, lr=1.2e-05, time_each_step=0.32s, eta=0:7:24\n",
      "2021-08-13 17:18:10 [INFO]\t[TRAIN] Epoch=72/100, Step=23/43, loss=11.972701, lr=1.2e-05, time_each_step=0.3s, eta=0:7:23\n",
      "2021-08-13 17:18:10 [INFO]\t[TRAIN] Epoch=72/100, Step=25/43, loss=13.002596, lr=1.2e-05, time_each_step=0.3s, eta=0:7:23\n",
      "2021-08-13 17:18:11 [INFO]\t[TRAIN] Epoch=72/100, Step=27/43, loss=10.592289, lr=1.2e-05, time_each_step=0.28s, eta=0:7:22\n",
      "2021-08-13 17:18:11 [INFO]\t[TRAIN] Epoch=72/100, Step=29/43, loss=11.67667, lr=1.2e-05, time_each_step=0.28s, eta=0:7:21\n",
      "2021-08-13 17:18:11 [INFO]\t[TRAIN] Epoch=72/100, Step=31/43, loss=6.390606, lr=1.2e-05, time_each_step=0.28s, eta=0:7:21\n",
      "2021-08-13 17:18:12 [INFO]\t[TRAIN] Epoch=72/100, Step=33/43, loss=10.815052, lr=1.2e-05, time_each_step=0.26s, eta=0:7:20\n",
      "2021-08-13 17:18:12 [INFO]\t[TRAIN] Epoch=72/100, Step=35/43, loss=9.665991, lr=1.2e-05, time_each_step=0.25s, eta=0:7:19\n",
      "2021-08-13 17:18:13 [INFO]\t[TRAIN] Epoch=72/100, Step=37/43, loss=17.651575, lr=1.2e-05, time_each_step=0.24s, eta=0:7:19\n",
      "2021-08-13 17:18:13 [INFO]\t[TRAIN] Epoch=72/100, Step=39/43, loss=8.722178, lr=1.2e-05, time_each_step=0.23s, eta=0:7:18\n",
      "2021-08-13 17:18:13 [INFO]\t[TRAIN] Epoch=72/100, Step=41/43, loss=8.188766, lr=1.2e-05, time_each_step=0.21s, eta=0:7:18\n",
      "2021-08-13 17:18:14 [INFO]\t[TRAIN] Epoch=72/100, Step=43/43, loss=9.474086, lr=1.2e-05, time_each_step=0.21s, eta=0:7:17\n",
      "2021-08-13 17:18:14 [INFO]\t[TRAIN] Epoch 72 finished, loss=10.616582, lr=1.3e-05 .\n",
      "2021-08-13 17:18:20 [INFO]\t[TRAIN] Epoch=73/100, Step=2/43, loss=5.08395, lr=1.2e-05, time_each_step=0.47s, eta=0:9:37\n",
      "2021-08-13 17:18:20 [INFO]\t[TRAIN] Epoch=73/100, Step=4/43, loss=13.156033, lr=1.2e-05, time_each_step=0.48s, eta=0:9:36\n",
      "2021-08-13 17:18:21 [INFO]\t[TRAIN] Epoch=73/100, Step=6/43, loss=8.778528, lr=1.2e-05, time_each_step=0.49s, eta=0:9:36\n",
      "2021-08-13 17:18:21 [INFO]\t[TRAIN] Epoch=73/100, Step=8/43, loss=13.351389, lr=1.2e-05, time_each_step=0.5s, eta=0:9:35\n",
      "2021-08-13 17:18:22 [INFO]\t[TRAIN] Epoch=73/100, Step=10/43, loss=8.085016, lr=1.2e-05, time_each_step=0.51s, eta=0:9:35\n",
      "2021-08-13 17:18:23 [INFO]\t[TRAIN] Epoch=73/100, Step=12/43, loss=8.618771, lr=1.2e-05, time_each_step=0.51s, eta=0:9:34\n",
      "2021-08-13 17:18:23 [INFO]\t[TRAIN] Epoch=73/100, Step=14/43, loss=12.088157, lr=1.2e-05, time_each_step=0.52s, eta=0:9:33\n",
      "2021-08-13 17:18:24 [INFO]\t[TRAIN] Epoch=73/100, Step=16/43, loss=10.84919, lr=1.2e-05, time_each_step=0.52s, eta=0:9:32\n",
      "2021-08-13 17:18:24 [INFO]\t[TRAIN] Epoch=73/100, Step=18/43, loss=10.06373, lr=1.2e-05, time_each_step=0.54s, eta=0:9:31\n",
      "2021-08-13 17:18:25 [INFO]\t[TRAIN] Epoch=73/100, Step=20/43, loss=11.602709, lr=1.2e-05, time_each_step=0.56s, eta=0:9:31\n",
      "2021-08-13 17:18:26 [INFO]\t[TRAIN] Epoch=73/100, Step=22/43, loss=12.56562, lr=1.2e-05, time_each_step=0.3s, eta=0:9:24\n",
      "2021-08-13 17:18:26 [INFO]\t[TRAIN] Epoch=73/100, Step=24/43, loss=10.434556, lr=1.2e-05, time_each_step=0.29s, eta=0:9:23\n",
      "2021-08-13 17:18:26 [INFO]\t[TRAIN] Epoch=73/100, Step=26/43, loss=9.691587, lr=1.2e-05, time_each_step=0.28s, eta=0:9:22\n",
      "2021-08-13 17:18:27 [INFO]\t[TRAIN] Epoch=73/100, Step=28/43, loss=7.800591, lr=1.2e-05, time_each_step=0.27s, eta=0:9:22\n",
      "2021-08-13 17:18:27 [INFO]\t[TRAIN] Epoch=73/100, Step=30/43, loss=10.255869, lr=1.2e-05, time_each_step=0.26s, eta=0:9:21\n",
      "2021-08-13 17:18:28 [INFO]\t[TRAIN] Epoch=73/100, Step=32/43, loss=9.735377, lr=1.2e-05, time_each_step=0.26s, eta=0:9:21\n",
      "2021-08-13 17:18:28 [INFO]\t[TRAIN] Epoch=73/100, Step=34/43, loss=11.31967, lr=1.2e-05, time_each_step=0.25s, eta=0:9:20\n",
      "2021-08-13 17:18:29 [INFO]\t[TRAIN] Epoch=73/100, Step=36/43, loss=8.45766, lr=1.2e-05, time_each_step=0.26s, eta=0:9:20\n",
      "2021-08-13 17:18:29 [INFO]\t[TRAIN] Epoch=73/100, Step=38/43, loss=9.014305, lr=1.2e-05, time_each_step=0.24s, eta=0:9:19\n",
      "2021-08-13 17:18:30 [INFO]\t[TRAIN] Epoch=73/100, Step=40/43, loss=11.395372, lr=1.2e-05, time_each_step=0.24s, eta=0:9:18\n",
      "2021-08-13 17:18:30 [INFO]\t[TRAIN] Epoch=73/100, Step=42/43, loss=9.875933, lr=1.2e-05, time_each_step=0.23s, eta=0:9:18\n",
      "2021-08-13 17:18:30 [INFO]\t[TRAIN] Epoch 73 finished, loss=10.186095, lr=1.3e-05 .\n",
      "2021-08-13 17:18:36 [INFO]\t[TRAIN] Epoch=74/100, Step=1/43, loss=10.441433, lr=1.2e-05, time_each_step=0.51s, eta=0:8:14\n",
      "2021-08-13 17:18:37 [INFO]\t[TRAIN] Epoch=74/100, Step=3/43, loss=9.826015, lr=1.2e-05, time_each_step=0.52s, eta=0:8:14\n",
      "2021-08-13 17:18:37 [INFO]\t[TRAIN] Epoch=74/100, Step=5/43, loss=8.896294, lr=1.2e-05, time_each_step=0.52s, eta=0:8:13\n",
      "2021-08-13 17:18:38 [INFO]\t[TRAIN] Epoch=74/100, Step=7/43, loss=10.967569, lr=1.2e-05, time_each_step=0.53s, eta=0:8:12\n",
      "2021-08-13 17:18:38 [INFO]\t[TRAIN] Epoch=74/100, Step=9/43, loss=9.485094, lr=1.2e-05, time_each_step=0.52s, eta=0:8:10\n",
      "2021-08-13 17:18:39 [INFO]\t[TRAIN] Epoch=74/100, Step=11/43, loss=9.668077, lr=1.2e-05, time_each_step=0.54s, eta=0:8:10\n",
      "2021-08-13 17:18:40 [INFO]\t[TRAIN] Epoch=74/100, Step=13/43, loss=7.969726, lr=1.2e-05, time_each_step=0.56s, eta=0:8:9\n",
      "2021-08-13 17:18:41 [INFO]\t[TRAIN] Epoch=74/100, Step=15/43, loss=8.145824, lr=1.2e-05, time_each_step=0.58s, eta=0:8:9\n",
      "2021-08-13 17:18:41 [INFO]\t[TRAIN] Epoch=74/100, Step=17/43, loss=7.422081, lr=1.2e-05, time_each_step=0.57s, eta=0:8:8\n",
      "2021-08-13 17:18:42 [INFO]\t[TRAIN] Epoch=74/100, Step=19/43, loss=11.068778, lr=1.2e-05, time_each_step=0.58s, eta=0:8:7\n",
      "2021-08-13 17:18:42 [INFO]\t[TRAIN] Epoch=74/100, Step=21/43, loss=10.518571, lr=1.2e-05, time_each_step=0.3s, eta=0:7:59\n",
      "2021-08-13 17:18:43 [INFO]\t[TRAIN] Epoch=74/100, Step=23/43, loss=7.269897, lr=1.2e-05, time_each_step=0.29s, eta=0:7:59\n",
      "2021-08-13 17:18:43 [INFO]\t[TRAIN] Epoch=74/100, Step=25/43, loss=9.936015, lr=1.2e-05, time_each_step=0.29s, eta=0:7:58\n",
      "2021-08-13 17:18:43 [INFO]\t[TRAIN] Epoch=74/100, Step=27/43, loss=10.046763, lr=1.2e-05, time_each_step=0.27s, eta=0:7:57\n",
      "2021-08-13 17:18:44 [INFO]\t[TRAIN] Epoch=74/100, Step=29/43, loss=12.23432, lr=1.2e-05, time_each_step=0.27s, eta=0:7:56\n",
      "2021-08-13 17:18:44 [INFO]\t[TRAIN] Epoch=74/100, Step=31/43, loss=12.331507, lr=1.2e-05, time_each_step=0.25s, eta=0:7:56\n",
      "2021-08-13 17:18:45 [INFO]\t[TRAIN] Epoch=74/100, Step=33/43, loss=10.081518, lr=1.2e-05, time_each_step=0.23s, eta=0:7:55\n",
      "2021-08-13 17:18:45 [INFO]\t[TRAIN] Epoch=74/100, Step=35/43, loss=14.438672, lr=1.2e-05, time_each_step=0.21s, eta=0:7:54\n",
      "2021-08-13 17:18:45 [INFO]\t[TRAIN] Epoch=74/100, Step=37/43, loss=9.839996, lr=1.2e-05, time_each_step=0.21s, eta=0:7:54\n",
      "2021-08-13 17:18:46 [INFO]\t[TRAIN] Epoch=74/100, Step=39/43, loss=11.939602, lr=1.2e-05, time_each_step=0.2s, eta=0:7:53\n",
      "2021-08-13 17:18:46 [INFO]\t[TRAIN] Epoch=74/100, Step=41/43, loss=8.651695, lr=1.2e-05, time_each_step=0.2s, eta=0:7:53\n",
      "2021-08-13 17:18:47 [INFO]\t[TRAIN] Epoch=74/100, Step=43/43, loss=8.681234, lr=1.2e-05, time_each_step=0.19s, eta=0:7:53\n",
      "2021-08-13 17:18:47 [INFO]\t[TRAIN] Epoch 74 finished, loss=10.284507, lr=1.3e-05 .\n",
      "2021-08-13 17:18:51 [INFO]\t[TRAIN] Epoch=75/100, Step=2/43, loss=14.198942, lr=1.2e-05, time_each_step=0.38s, eta=0:7:44\n",
      "2021-08-13 17:18:51 [INFO]\t[TRAIN] Epoch=75/100, Step=4/43, loss=12.195723, lr=1.2e-05, time_each_step=0.4s, eta=0:7:44\n",
      "2021-08-13 17:18:52 [INFO]\t[TRAIN] Epoch=75/100, Step=6/43, loss=8.600376, lr=1.2e-05, time_each_step=0.41s, eta=0:7:44\n",
      "2021-08-13 17:18:52 [INFO]\t[TRAIN] Epoch=75/100, Step=8/43, loss=12.525469, lr=1.2e-05, time_each_step=0.42s, eta=0:7:43\n",
      "2021-08-13 17:18:53 [INFO]\t[TRAIN] Epoch=75/100, Step=10/43, loss=10.67462, lr=1.2e-05, time_each_step=0.42s, eta=0:7:42\n",
      "2021-08-13 17:18:54 [INFO]\t[TRAIN] Epoch=75/100, Step=12/43, loss=8.60088, lr=1.2e-05, time_each_step=0.44s, eta=0:7:42\n",
      "2021-08-13 17:18:54 [INFO]\t[TRAIN] Epoch=75/100, Step=14/43, loss=9.427768, lr=1.2e-05, time_each_step=0.44s, eta=0:7:41\n",
      "2021-08-13 17:18:55 [INFO]\t[TRAIN] Epoch=75/100, Step=16/43, loss=9.392231, lr=1.2e-05, time_each_step=0.45s, eta=0:7:41\n",
      "2021-08-13 17:18:55 [INFO]\t[TRAIN] Epoch=75/100, Step=18/43, loss=11.220237, lr=1.2e-05, time_each_step=0.45s, eta=0:7:40\n",
      "2021-08-13 17:18:56 [INFO]\t[TRAIN] Epoch=75/100, Step=20/43, loss=9.495934, lr=1.2e-05, time_each_step=0.46s, eta=0:7:39\n",
      "2021-08-13 17:18:56 [INFO]\t[TRAIN] Epoch=75/100, Step=22/43, loss=8.814816, lr=1.2e-05, time_each_step=0.28s, eta=0:7:34\n",
      "2021-08-13 17:18:57 [INFO]\t[TRAIN] Epoch=75/100, Step=24/43, loss=13.556206, lr=1.2e-05, time_each_step=0.28s, eta=0:7:34\n",
      "2021-08-13 17:18:57 [INFO]\t[TRAIN] Epoch=75/100, Step=26/43, loss=10.554026, lr=1.2e-05, time_each_step=0.26s, eta=0:7:33\n",
      "2021-08-13 17:18:58 [INFO]\t[TRAIN] Epoch=75/100, Step=28/43, loss=9.001135, lr=1.2e-05, time_each_step=0.26s, eta=0:7:32\n",
      "2021-08-13 17:18:58 [INFO]\t[TRAIN] Epoch=75/100, Step=30/43, loss=10.257135, lr=1.2e-05, time_each_step=0.26s, eta=0:7:32\n",
      "2021-08-13 17:18:59 [INFO]\t[TRAIN] Epoch=75/100, Step=32/43, loss=8.00031, lr=1.2e-05, time_each_step=0.25s, eta=0:7:31\n",
      "2021-08-13 17:18:59 [INFO]\t[TRAIN] Epoch=75/100, Step=34/43, loss=8.428726, lr=1.2e-05, time_each_step=0.25s, eta=0:7:31\n",
      "2021-08-13 17:19:00 [INFO]\t[TRAIN] Epoch=75/100, Step=36/43, loss=9.198267, lr=1.2e-05, time_each_step=0.25s, eta=0:7:30\n",
      "2021-08-13 17:19:00 [INFO]\t[TRAIN] Epoch=75/100, Step=38/43, loss=11.325804, lr=1.2e-05, time_each_step=0.25s, eta=0:7:30\n",
      "2021-08-13 17:19:00 [INFO]\t[TRAIN] Epoch=75/100, Step=40/43, loss=8.989996, lr=1.2e-05, time_each_step=0.24s, eta=0:7:29\n",
      "2021-08-13 17:19:01 [INFO]\t[TRAIN] Epoch=75/100, Step=42/43, loss=10.965094, lr=1.2e-05, time_each_step=0.22s, eta=0:7:29\n",
      "2021-08-13 17:19:01 [INFO]\t[TRAIN] Epoch 75 finished, loss=10.826797, lr=1.3e-05 .\n",
      "2021-08-13 17:19:12 [INFO]\t[TRAIN] Epoch=76/100, Step=1/43, loss=9.510122, lr=1.2e-05, time_each_step=0.74s, eta=0:6:58\n",
      "2021-08-13 17:19:12 [INFO]\t[TRAIN] Epoch=76/100, Step=3/43, loss=7.596786, lr=1.2e-05, time_each_step=0.75s, eta=0:6:57\n",
      "2021-08-13 17:19:13 [INFO]\t[TRAIN] Epoch=76/100, Step=5/43, loss=10.887912, lr=1.2e-05, time_each_step=0.77s, eta=0:6:56\n",
      "2021-08-13 17:19:14 [INFO]\t[TRAIN] Epoch=76/100, Step=7/43, loss=9.231071, lr=1.2e-05, time_each_step=0.78s, eta=0:6:55\n",
      "2021-08-13 17:19:15 [INFO]\t[TRAIN] Epoch=76/100, Step=9/43, loss=11.902739, lr=1.2e-05, time_each_step=0.79s, eta=0:6:54\n",
      "2021-08-13 17:19:15 [INFO]\t[TRAIN] Epoch=76/100, Step=11/43, loss=7.904202, lr=1.2e-05, time_each_step=0.81s, eta=0:6:53\n",
      "2021-08-13 17:19:16 [INFO]\t[TRAIN] Epoch=76/100, Step=13/43, loss=12.814359, lr=1.2e-05, time_each_step=0.81s, eta=0:6:51\n",
      "2021-08-13 17:19:17 [INFO]\t[TRAIN] Epoch=76/100, Step=15/43, loss=8.635248, lr=1.2e-05, time_each_step=0.83s, eta=0:6:50\n",
      "2021-08-13 17:19:17 [INFO]\t[TRAIN] Epoch=76/100, Step=17/43, loss=9.067744, lr=1.2e-05, time_each_step=0.85s, eta=0:6:49\n",
      "2021-08-13 17:19:18 [INFO]\t[TRAIN] Epoch=76/100, Step=19/43, loss=15.158857, lr=1.2e-05, time_each_step=0.86s, eta=0:6:48\n",
      "2021-08-13 17:19:18 [INFO]\t[TRAIN] Epoch=76/100, Step=21/43, loss=13.94233, lr=1.2e-05, time_each_step=0.33s, eta=0:6:34\n",
      "2021-08-13 17:19:19 [INFO]\t[TRAIN] Epoch=76/100, Step=23/43, loss=7.841909, lr=1.2e-05, time_each_step=0.33s, eta=0:6:34\n",
      "2021-08-13 17:19:19 [INFO]\t[TRAIN] Epoch=76/100, Step=25/43, loss=8.603215, lr=1.2e-05, time_each_step=0.3s, eta=0:6:32\n",
      "2021-08-13 17:19:20 [INFO]\t[TRAIN] Epoch=76/100, Step=27/43, loss=10.911312, lr=1.2e-05, time_each_step=0.29s, eta=0:6:32\n",
      "2021-08-13 17:19:20 [INFO]\t[TRAIN] Epoch=76/100, Step=29/43, loss=10.875904, lr=1.2e-05, time_each_step=0.27s, eta=0:6:31\n",
      "2021-08-13 17:19:20 [INFO]\t[TRAIN] Epoch=76/100, Step=31/43, loss=10.927799, lr=1.2e-05, time_each_step=0.25s, eta=0:6:30\n",
      "2021-08-13 17:19:21 [INFO]\t[TRAIN] Epoch=76/100, Step=33/43, loss=16.149647, lr=1.2e-05, time_each_step=0.26s, eta=0:6:30\n",
      "2021-08-13 17:19:21 [INFO]\t[TRAIN] Epoch=76/100, Step=35/43, loss=8.193758, lr=1.2e-05, time_each_step=0.24s, eta=0:6:29\n",
      "2021-08-13 17:19:22 [INFO]\t[TRAIN] Epoch=76/100, Step=37/43, loss=7.558369, lr=1.2e-05, time_each_step=0.21s, eta=0:6:28\n",
      "2021-08-13 17:19:22 [INFO]\t[TRAIN] Epoch=76/100, Step=39/43, loss=7.566096, lr=1.2e-05, time_each_step=0.21s, eta=0:6:28\n",
      "2021-08-13 17:19:23 [INFO]\t[TRAIN] Epoch=76/100, Step=41/43, loss=8.237719, lr=1.2e-05, time_each_step=0.22s, eta=0:6:27\n",
      "2021-08-13 17:19:23 [INFO]\t[TRAIN] Epoch=76/100, Step=43/43, loss=7.972685, lr=1.2e-05, time_each_step=0.21s, eta=0:6:27\n",
      "2021-08-13 17:19:23 [INFO]\t[TRAIN] Epoch 76 finished, loss=10.105831, lr=1.3e-05 .\n",
      "2021-08-13 17:19:30 [INFO]\t[TRAIN] Epoch=77/100, Step=2/43, loss=9.832062, lr=1.2e-05, time_each_step=0.51s, eta=0:9:36\n",
      "2021-08-13 17:19:30 [INFO]\t[TRAIN] Epoch=77/100, Step=4/43, loss=9.086464, lr=1.2e-05, time_each_step=0.52s, eta=0:9:35\n",
      "2021-08-13 17:19:31 [INFO]\t[TRAIN] Epoch=77/100, Step=6/43, loss=11.056175, lr=1.2e-05, time_each_step=0.54s, eta=0:9:35\n",
      "2021-08-13 17:19:31 [INFO]\t[TRAIN] Epoch=77/100, Step=8/43, loss=6.624968, lr=1.2e-05, time_each_step=0.55s, eta=0:9:34\n",
      "2021-08-13 17:19:32 [INFO]\t[TRAIN] Epoch=77/100, Step=10/43, loss=12.159979, lr=1.2e-05, time_each_step=0.55s, eta=0:9:33\n",
      "2021-08-13 17:19:33 [INFO]\t[TRAIN] Epoch=77/100, Step=12/43, loss=10.848914, lr=1.2e-05, time_each_step=0.56s, eta=0:9:32\n",
      "2021-08-13 17:19:33 [INFO]\t[TRAIN] Epoch=77/100, Step=14/43, loss=11.755587, lr=1.2e-05, time_each_step=0.58s, eta=0:9:31\n",
      "2021-08-13 17:19:34 [INFO]\t[TRAIN] Epoch=77/100, Step=16/43, loss=7.878005, lr=1.2e-05, time_each_step=0.59s, eta=0:9:31\n",
      "2021-08-13 17:19:34 [INFO]\t[TRAIN] Epoch=77/100, Step=18/43, loss=11.094995, lr=1.2e-05, time_each_step=0.59s, eta=0:9:29\n",
      "2021-08-13 17:19:35 [INFO]\t[TRAIN] Epoch=77/100, Step=20/43, loss=8.327482, lr=1.2e-05, time_each_step=0.59s, eta=0:9:28\n",
      "2021-08-13 17:19:35 [INFO]\t[TRAIN] Epoch=77/100, Step=22/43, loss=13.644964, lr=1.2e-05, time_each_step=0.3s, eta=0:9:21\n",
      "2021-08-13 17:19:36 [INFO]\t[TRAIN] Epoch=77/100, Step=24/43, loss=9.404535, lr=1.2e-05, time_each_step=0.31s, eta=0:9:21\n",
      "2021-08-13 17:19:36 [INFO]\t[TRAIN] Epoch=77/100, Step=26/43, loss=11.002399, lr=1.2e-05, time_each_step=0.28s, eta=0:9:19\n",
      "2021-08-13 17:19:37 [INFO]\t[TRAIN] Epoch=77/100, Step=28/43, loss=9.676343, lr=1.2e-05, time_each_step=0.28s, eta=0:9:19\n",
      "2021-08-13 17:19:38 [INFO]\t[TRAIN] Epoch=77/100, Step=30/43, loss=10.120357, lr=1.2e-05, time_each_step=0.27s, eta=0:9:18\n",
      "2021-08-13 17:19:38 [INFO]\t[TRAIN] Epoch=77/100, Step=32/43, loss=11.806266, lr=1.2e-05, time_each_step=0.26s, eta=0:9:17\n",
      "2021-08-13 17:19:38 [INFO]\t[TRAIN] Epoch=77/100, Step=34/43, loss=13.367788, lr=1.2e-05, time_each_step=0.25s, eta=0:9:17\n",
      "2021-08-13 17:19:39 [INFO]\t[TRAIN] Epoch=77/100, Step=36/43, loss=6.468862, lr=1.2e-05, time_each_step=0.25s, eta=0:9:16\n",
      "2021-08-13 17:19:39 [INFO]\t[TRAIN] Epoch=77/100, Step=38/43, loss=10.358616, lr=1.2e-05, time_each_step=0.25s, eta=0:9:16\n",
      "2021-08-13 17:19:40 [INFO]\t[TRAIN] Epoch=77/100, Step=40/43, loss=6.571411, lr=1.2e-05, time_each_step=0.24s, eta=0:9:15\n",
      "2021-08-13 17:19:40 [INFO]\t[TRAIN] Epoch=77/100, Step=42/43, loss=9.797974, lr=1.2e-05, time_each_step=0.23s, eta=0:9:15\n",
      "2021-08-13 17:19:40 [INFO]\t[TRAIN] Epoch 77 finished, loss=10.255728, lr=1.3e-05 .\n",
      "2021-08-13 17:19:48 [INFO]\t[TRAIN] Epoch=78/100, Step=1/43, loss=10.143853, lr=1.2e-05, time_each_step=0.57s, eta=0:7:23\n",
      "2021-08-13 17:19:48 [INFO]\t[TRAIN] Epoch=78/100, Step=3/43, loss=7.689987, lr=1.2e-05, time_each_step=0.58s, eta=0:7:22\n",
      "2021-08-13 17:19:49 [INFO]\t[TRAIN] Epoch=78/100, Step=5/43, loss=11.20203, lr=1.2e-05, time_each_step=0.58s, eta=0:7:21\n",
      "2021-08-13 17:19:50 [INFO]\t[TRAIN] Epoch=78/100, Step=7/43, loss=10.622479, lr=1.2e-05, time_each_step=0.6s, eta=0:7:20\n",
      "2021-08-13 17:19:50 [INFO]\t[TRAIN] Epoch=78/100, Step=9/43, loss=9.025399, lr=1.2e-05, time_each_step=0.62s, eta=0:7:20\n",
      "2021-08-13 17:19:51 [INFO]\t[TRAIN] Epoch=78/100, Step=11/43, loss=7.955155, lr=1.2e-05, time_each_step=0.63s, eta=0:7:19\n",
      "2021-08-13 17:19:51 [INFO]\t[TRAIN] Epoch=78/100, Step=13/43, loss=13.722828, lr=1.2e-05, time_each_step=0.62s, eta=0:7:17\n",
      "2021-08-13 17:19:52 [INFO]\t[TRAIN] Epoch=78/100, Step=15/43, loss=6.933421, lr=1.2e-05, time_each_step=0.62s, eta=0:7:16\n",
      "2021-08-13 17:19:52 [INFO]\t[TRAIN] Epoch=78/100, Step=17/43, loss=15.188294, lr=1.2e-05, time_each_step=0.63s, eta=0:7:15\n",
      "2021-08-13 17:19:53 [INFO]\t[TRAIN] Epoch=78/100, Step=19/43, loss=9.844285, lr=1.2e-05, time_each_step=0.64s, eta=0:7:14\n",
      "2021-08-13 17:19:53 [INFO]\t[TRAIN] Epoch=78/100, Step=21/43, loss=7.755638, lr=1.2e-05, time_each_step=0.29s, eta=0:7:5\n",
      "2021-08-13 17:19:54 [INFO]\t[TRAIN] Epoch=78/100, Step=23/43, loss=9.788158, lr=1.2e-05, time_each_step=0.3s, eta=0:7:5\n",
      "2021-08-13 17:19:55 [INFO]\t[TRAIN] Epoch=78/100, Step=25/43, loss=5.980923, lr=1.2e-05, time_each_step=0.29s, eta=0:7:4\n",
      "2021-08-13 17:19:55 [INFO]\t[TRAIN] Epoch=78/100, Step=27/43, loss=11.431492, lr=1.2e-05, time_each_step=0.27s, eta=0:7:3\n",
      "2021-08-13 17:19:55 [INFO]\t[TRAIN] Epoch=78/100, Step=29/43, loss=7.839042, lr=1.2e-05, time_each_step=0.26s, eta=0:7:2\n",
      "2021-08-13 17:19:56 [INFO]\t[TRAIN] Epoch=78/100, Step=31/43, loss=7.31053, lr=1.2e-05, time_each_step=0.24s, eta=0:7:2\n",
      "2021-08-13 17:19:56 [INFO]\t[TRAIN] Epoch=78/100, Step=33/43, loss=12.111864, lr=1.2e-05, time_each_step=0.24s, eta=0:7:1\n",
      "2021-08-13 17:19:56 [INFO]\t[TRAIN] Epoch=78/100, Step=35/43, loss=9.993774, lr=1.2e-05, time_each_step=0.23s, eta=0:7:1\n",
      "2021-08-13 17:19:57 [INFO]\t[TRAIN] Epoch=78/100, Step=37/43, loss=9.685627, lr=1.2e-05, time_each_step=0.23s, eta=0:7:0\n",
      "2021-08-13 17:19:57 [INFO]\t[TRAIN] Epoch=78/100, Step=39/43, loss=8.280391, lr=1.2e-05, time_each_step=0.22s, eta=0:7:0\n",
      "2021-08-13 17:19:58 [INFO]\t[TRAIN] Epoch=78/100, Step=41/43, loss=11.886181, lr=1.2e-05, time_each_step=0.22s, eta=0:6:59\n",
      "2021-08-13 17:19:58 [INFO]\t[TRAIN] Epoch=78/100, Step=43/43, loss=12.226425, lr=1.2e-05, time_each_step=0.21s, eta=0:6:59\n",
      "2021-08-13 17:19:58 [INFO]\t[TRAIN] Epoch 78 finished, loss=9.79469, lr=1.3e-05 .\n",
      "2021-08-13 17:20:03 [INFO]\t[TRAIN] Epoch=79/100, Step=2/43, loss=10.693119, lr=1.2e-05, time_each_step=0.45s, eta=0:7:20\n",
      "2021-08-13 17:20:04 [INFO]\t[TRAIN] Epoch=79/100, Step=4/43, loss=10.974298, lr=1.2e-05, time_each_step=0.46s, eta=0:7:20\n",
      "2021-08-13 17:20:05 [INFO]\t[TRAIN] Epoch=79/100, Step=6/43, loss=13.33075, lr=1.2e-05, time_each_step=0.48s, eta=0:7:19\n",
      "2021-08-13 17:20:05 [INFO]\t[TRAIN] Epoch=79/100, Step=8/43, loss=8.78524, lr=1.2e-05, time_each_step=0.49s, eta=0:7:19\n",
      "2021-08-13 17:20:06 [INFO]\t[TRAIN] Epoch=79/100, Step=10/43, loss=8.383433, lr=1.2e-05, time_each_step=0.5s, eta=0:7:18\n",
      "2021-08-13 17:20:07 [INFO]\t[TRAIN] Epoch=79/100, Step=12/43, loss=8.61844, lr=1.2e-05, time_each_step=0.51s, eta=0:7:17\n",
      "2021-08-13 17:20:07 [INFO]\t[TRAIN] Epoch=79/100, Step=14/43, loss=6.930039, lr=1.2e-05, time_each_step=0.52s, eta=0:7:17\n",
      "2021-08-13 17:20:08 [INFO]\t[TRAIN] Epoch=79/100, Step=16/43, loss=9.345928, lr=1.2e-05, time_each_step=0.53s, eta=0:7:16\n",
      "2021-08-13 17:20:08 [INFO]\t[TRAIN] Epoch=79/100, Step=18/43, loss=8.505185, lr=1.2e-05, time_each_step=0.52s, eta=0:7:14\n",
      "2021-08-13 17:20:09 [INFO]\t[TRAIN] Epoch=79/100, Step=20/43, loss=12.298162, lr=1.2e-05, time_each_step=0.54s, eta=0:7:14\n",
      "2021-08-13 17:20:10 [INFO]\t[TRAIN] Epoch=79/100, Step=22/43, loss=10.67508, lr=1.2e-05, time_each_step=0.3s, eta=0:7:8\n",
      "2021-08-13 17:20:10 [INFO]\t[TRAIN] Epoch=79/100, Step=24/43, loss=8.363995, lr=1.2e-05, time_each_step=0.28s, eta=0:7:7\n",
      "2021-08-13 17:20:10 [INFO]\t[TRAIN] Epoch=79/100, Step=26/43, loss=9.679938, lr=1.2e-05, time_each_step=0.26s, eta=0:7:6\n",
      "2021-08-13 17:20:11 [INFO]\t[TRAIN] Epoch=79/100, Step=28/43, loss=13.220137, lr=1.2e-05, time_each_step=0.25s, eta=0:7:5\n",
      "2021-08-13 17:20:11 [INFO]\t[TRAIN] Epoch=79/100, Step=30/43, loss=11.909439, lr=1.2e-05, time_each_step=0.24s, eta=0:7:5\n",
      "2021-08-13 17:20:11 [INFO]\t[TRAIN] Epoch=79/100, Step=32/43, loss=6.777117, lr=1.2e-05, time_each_step=0.23s, eta=0:7:4\n",
      "2021-08-13 17:20:12 [INFO]\t[TRAIN] Epoch=79/100, Step=34/43, loss=10.438658, lr=1.2e-05, time_each_step=0.22s, eta=0:7:3\n",
      "2021-08-13 17:20:12 [INFO]\t[TRAIN] Epoch=79/100, Step=36/43, loss=11.908935, lr=1.2e-05, time_each_step=0.22s, eta=0:7:3\n",
      "2021-08-13 17:20:13 [INFO]\t[TRAIN] Epoch=79/100, Step=38/43, loss=8.821688, lr=1.2e-05, time_each_step=0.21s, eta=0:7:3\n",
      "2021-08-13 17:20:13 [INFO]\t[TRAIN] Epoch=79/100, Step=40/43, loss=9.371721, lr=1.2e-05, time_each_step=0.2s, eta=0:7:2\n",
      "2021-08-13 17:20:13 [INFO]\t[TRAIN] Epoch=79/100, Step=42/43, loss=9.951133, lr=1.2e-05, time_each_step=0.19s, eta=0:7:2\n",
      "2021-08-13 17:20:13 [INFO]\t[TRAIN] Epoch 79 finished, loss=10.050387, lr=1.3e-05 .\n",
      "2021-08-13 17:20:20 [INFO]\t[TRAIN] Epoch=80/100, Step=1/43, loss=13.64555, lr=1.2e-05, time_each_step=0.52s, eta=0:6:10\n",
      "2021-08-13 17:20:21 [INFO]\t[TRAIN] Epoch=80/100, Step=3/43, loss=7.065106, lr=1.2e-05, time_each_step=0.55s, eta=0:6:11\n",
      "2021-08-13 17:20:22 [INFO]\t[TRAIN] Epoch=80/100, Step=5/43, loss=9.294989, lr=1.2e-05, time_each_step=0.56s, eta=0:6:10\n",
      "2021-08-13 17:20:22 [INFO]\t[TRAIN] Epoch=80/100, Step=7/43, loss=14.80973, lr=1.2e-05, time_each_step=0.57s, eta=0:6:9\n",
      "2021-08-13 17:20:23 [INFO]\t[TRAIN] Epoch=80/100, Step=9/43, loss=10.772776, lr=1.2e-05, time_each_step=0.59s, eta=0:6:9\n",
      "2021-08-13 17:20:24 [INFO]\t[TRAIN] Epoch=80/100, Step=11/43, loss=9.580713, lr=1.2e-05, time_each_step=0.59s, eta=0:6:7\n",
      "2021-08-13 17:20:24 [INFO]\t[TRAIN] Epoch=80/100, Step=13/43, loss=7.734487, lr=1.2e-05, time_each_step=0.58s, eta=0:6:6\n",
      "2021-08-13 17:20:25 [INFO]\t[TRAIN] Epoch=80/100, Step=15/43, loss=12.105704, lr=1.2e-05, time_each_step=0.6s, eta=0:6:5\n",
      "2021-08-13 17:20:25 [INFO]\t[TRAIN] Epoch=80/100, Step=17/43, loss=9.599701, lr=1.2e-05, time_each_step=0.6s, eta=0:6:4\n",
      "2021-08-13 17:20:26 [INFO]\t[TRAIN] Epoch=80/100, Step=19/43, loss=7.758393, lr=1.2e-05, time_each_step=0.62s, eta=0:6:3\n",
      "2021-08-13 17:20:26 [INFO]\t[TRAIN] Epoch=80/100, Step=21/43, loss=8.976887, lr=1.2e-05, time_each_step=0.3s, eta=0:5:55\n",
      "2021-08-13 17:20:27 [INFO]\t[TRAIN] Epoch=80/100, Step=23/43, loss=8.396412, lr=1.2e-05, time_each_step=0.28s, eta=0:5:54\n",
      "2021-08-13 17:20:27 [INFO]\t[TRAIN] Epoch=80/100, Step=25/43, loss=14.425636, lr=1.2e-05, time_each_step=0.28s, eta=0:5:54\n",
      "2021-08-13 17:20:28 [INFO]\t[TRAIN] Epoch=80/100, Step=27/43, loss=9.390627, lr=1.2e-05, time_each_step=0.28s, eta=0:5:53\n",
      "2021-08-13 17:20:28 [INFO]\t[TRAIN] Epoch=80/100, Step=29/43, loss=7.36346, lr=1.2e-05, time_each_step=0.26s, eta=0:5:52\n",
      "2021-08-13 17:20:29 [INFO]\t[TRAIN] Epoch=80/100, Step=31/43, loss=11.728104, lr=1.2e-05, time_each_step=0.25s, eta=0:5:52\n",
      "2021-08-13 17:20:29 [INFO]\t[TRAIN] Epoch=80/100, Step=33/43, loss=7.374114, lr=1.2e-05, time_each_step=0.26s, eta=0:5:51\n",
      "2021-08-13 17:20:29 [INFO]\t[TRAIN] Epoch=80/100, Step=35/43, loss=7.07541, lr=1.2e-05, time_each_step=0.24s, eta=0:5:50\n",
      "2021-08-13 17:20:30 [INFO]\t[TRAIN] Epoch=80/100, Step=37/43, loss=12.829588, lr=1.2e-05, time_each_step=0.24s, eta=0:5:50\n",
      "2021-08-13 17:20:30 [INFO]\t[TRAIN] Epoch=80/100, Step=39/43, loss=9.906402, lr=1.2e-05, time_each_step=0.23s, eta=0:5:49\n",
      "2021-08-13 17:20:30 [INFO]\t[TRAIN] Epoch=80/100, Step=41/43, loss=9.690763, lr=1.2e-05, time_each_step=0.21s, eta=0:5:49\n",
      "2021-08-13 17:20:31 [INFO]\t[TRAIN] Epoch=80/100, Step=43/43, loss=9.465088, lr=1.2e-05, time_each_step=0.21s, eta=0:5:49\n",
      "2021-08-13 17:20:31 [INFO]\t[TRAIN] Epoch 80 finished, loss=10.10187, lr=1.3e-05 .\n",
      "2021-08-13 17:20:31 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:08<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:20:40 [INFO]\t[EVAL] Finished, Epoch=80, bbox_map=63.759846 .\n",
      "2021-08-13 17:20:42 [INFO]\tModel saved in output/yolov3_darknet53/best_model.\n",
      "2021-08-13 17:20:43 [INFO]\tModel saved in output/yolov3_darknet53/epoch_80.\n",
      "2021-08-13 17:20:43 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_80, bbox_map=63.75984622249753\n",
      "2021-08-13 17:20:47 [INFO]\t[TRAIN] Epoch=81/100, Step=2/43, loss=16.952263, lr=1e-06, time_each_step=0.4s, eta=0:6:14\n",
      "2021-08-13 17:20:48 [INFO]\t[TRAIN] Epoch=81/100, Step=4/43, loss=10.506239, lr=1e-06, time_each_step=0.4s, eta=0:6:13\n",
      "2021-08-13 17:20:49 [INFO]\t[TRAIN] Epoch=81/100, Step=6/43, loss=9.940143, lr=1e-06, time_each_step=0.42s, eta=0:6:13\n",
      "2021-08-13 17:20:49 [INFO]\t[TRAIN] Epoch=81/100, Step=8/43, loss=8.139067, lr=1e-06, time_each_step=0.43s, eta=0:6:13\n",
      "2021-08-13 17:20:49 [INFO]\t[TRAIN] Epoch=81/100, Step=10/43, loss=9.713858, lr=1e-06, time_each_step=0.42s, eta=0:6:11\n",
      "2021-08-13 17:20:50 [INFO]\t[TRAIN] Epoch=81/100, Step=12/43, loss=10.699746, lr=1e-06, time_each_step=0.43s, eta=0:6:11\n",
      "2021-08-13 17:20:51 [INFO]\t[TRAIN] Epoch=81/100, Step=14/43, loss=9.328005, lr=1e-06, time_each_step=0.46s, eta=0:6:11\n",
      "2021-08-13 17:20:51 [INFO]\t[TRAIN] Epoch=81/100, Step=16/43, loss=13.506958, lr=1e-06, time_each_step=0.46s, eta=0:6:10\n",
      "2021-08-13 17:20:52 [INFO]\t[TRAIN] Epoch=81/100, Step=18/43, loss=8.631515, lr=1e-06, time_each_step=0.47s, eta=0:6:10\n",
      "2021-08-13 17:20:52 [INFO]\t[TRAIN] Epoch=81/100, Step=20/43, loss=11.490664, lr=1e-06, time_each_step=0.47s, eta=0:6:9\n",
      "2021-08-13 17:20:53 [INFO]\t[TRAIN] Epoch=81/100, Step=22/43, loss=14.692629, lr=1e-06, time_each_step=0.28s, eta=0:6:4\n",
      "2021-08-13 17:20:53 [INFO]\t[TRAIN] Epoch=81/100, Step=24/43, loss=11.722217, lr=1e-06, time_each_step=0.27s, eta=0:6:3\n",
      "2021-08-13 17:20:54 [INFO]\t[TRAIN] Epoch=81/100, Step=26/43, loss=7.120873, lr=1e-06, time_each_step=0.25s, eta=0:6:2\n",
      "2021-08-13 17:20:54 [INFO]\t[TRAIN] Epoch=81/100, Step=28/43, loss=10.27745, lr=1e-06, time_each_step=0.24s, eta=0:6:1\n",
      "2021-08-13 17:20:54 [INFO]\t[TRAIN] Epoch=81/100, Step=30/43, loss=8.415392, lr=1e-06, time_each_step=0.24s, eta=0:6:1\n",
      "2021-08-13 17:20:55 [INFO]\t[TRAIN] Epoch=81/100, Step=32/43, loss=10.130926, lr=1e-06, time_each_step=0.23s, eta=0:6:0\n",
      "2021-08-13 17:20:55 [INFO]\t[TRAIN] Epoch=81/100, Step=34/43, loss=12.039176, lr=1e-06, time_each_step=0.21s, eta=0:6:0\n",
      "2021-08-13 17:20:56 [INFO]\t[TRAIN] Epoch=81/100, Step=36/43, loss=11.579945, lr=1e-06, time_each_step=0.2s, eta=0:5:59\n",
      "2021-08-13 17:20:56 [INFO]\t[TRAIN] Epoch=81/100, Step=38/43, loss=8.933187, lr=1e-06, time_each_step=0.19s, eta=0:5:59\n",
      "2021-08-13 17:20:56 [INFO]\t[TRAIN] Epoch=81/100, Step=40/43, loss=6.145899, lr=1e-06, time_each_step=0.19s, eta=0:5:58\n",
      "2021-08-13 17:20:57 [INFO]\t[TRAIN] Epoch=81/100, Step=42/43, loss=10.201134, lr=1e-06, time_each_step=0.19s, eta=0:5:58\n",
      "2021-08-13 17:20:57 [INFO]\t[TRAIN] Epoch 81 finished, loss=10.524671, lr=1e-06 .\n",
      "2021-08-13 17:21:04 [INFO]\t[TRAIN] Epoch=82/100, Step=1/43, loss=8.451494, lr=1e-06, time_each_step=0.51s, eta=0:4:54\n",
      "2021-08-13 17:21:04 [INFO]\t[TRAIN] Epoch=82/100, Step=3/43, loss=7.26078, lr=1e-06, time_each_step=0.52s, eta=0:4:54\n",
      "2021-08-13 17:21:05 [INFO]\t[TRAIN] Epoch=82/100, Step=5/43, loss=12.778126, lr=1e-06, time_each_step=0.54s, eta=0:4:54\n",
      "2021-08-13 17:21:06 [INFO]\t[TRAIN] Epoch=82/100, Step=7/43, loss=9.136889, lr=1e-06, time_each_step=0.56s, eta=0:4:53\n",
      "2021-08-13 17:21:06 [INFO]\t[TRAIN] Epoch=82/100, Step=9/43, loss=7.830869, lr=1e-06, time_each_step=0.58s, eta=0:4:53\n",
      "2021-08-13 17:21:07 [INFO]\t[TRAIN] Epoch=82/100, Step=11/43, loss=6.424704, lr=1e-06, time_each_step=0.58s, eta=0:4:52\n",
      "2021-08-13 17:21:08 [INFO]\t[TRAIN] Epoch=82/100, Step=13/43, loss=6.345528, lr=1e-06, time_each_step=0.61s, eta=0:4:51\n",
      "2021-08-13 17:21:08 [INFO]\t[TRAIN] Epoch=82/100, Step=15/43, loss=9.647119, lr=1e-06, time_each_step=0.63s, eta=0:4:50\n",
      "2021-08-13 17:21:09 [INFO]\t[TRAIN] Epoch=82/100, Step=17/43, loss=10.147421, lr=1e-06, time_each_step=0.62s, eta=0:4:49\n",
      "2021-08-13 17:21:09 [INFO]\t[TRAIN] Epoch=82/100, Step=19/43, loss=12.399033, lr=1e-06, time_each_step=0.63s, eta=0:4:48\n",
      "2021-08-13 17:21:10 [INFO]\t[TRAIN] Epoch=82/100, Step=21/43, loss=9.265316, lr=1e-06, time_each_step=0.31s, eta=0:4:40\n",
      "2021-08-13 17:21:10 [INFO]\t[TRAIN] Epoch=82/100, Step=23/43, loss=11.897718, lr=1e-06, time_each_step=0.31s, eta=0:4:39\n",
      "2021-08-13 17:21:11 [INFO]\t[TRAIN] Epoch=82/100, Step=25/43, loss=14.251596, lr=1e-06, time_each_step=0.3s, eta=0:4:38\n",
      "2021-08-13 17:21:11 [INFO]\t[TRAIN] Epoch=82/100, Step=27/43, loss=10.958675, lr=1e-06, time_each_step=0.29s, eta=0:4:37\n",
      "2021-08-13 17:21:12 [INFO]\t[TRAIN] Epoch=82/100, Step=29/43, loss=9.870213, lr=1e-06, time_each_step=0.26s, eta=0:4:37\n",
      "2021-08-13 17:21:12 [INFO]\t[TRAIN] Epoch=82/100, Step=31/43, loss=7.970199, lr=1e-06, time_each_step=0.26s, eta=0:4:36\n",
      "2021-08-13 17:21:12 [INFO]\t[TRAIN] Epoch=82/100, Step=33/43, loss=14.293158, lr=1e-06, time_each_step=0.23s, eta=0:4:35\n",
      "2021-08-13 17:21:13 [INFO]\t[TRAIN] Epoch=82/100, Step=35/43, loss=13.701067, lr=1e-06, time_each_step=0.23s, eta=0:4:35\n",
      "2021-08-13 17:21:13 [INFO]\t[TRAIN] Epoch=82/100, Step=37/43, loss=12.586957, lr=1e-06, time_each_step=0.22s, eta=0:4:34\n",
      "2021-08-13 17:21:13 [INFO]\t[TRAIN] Epoch=82/100, Step=39/43, loss=12.418934, lr=1e-06, time_each_step=0.21s, eta=0:4:34\n",
      "2021-08-13 17:21:14 [INFO]\t[TRAIN] Epoch=82/100, Step=41/43, loss=16.556175, lr=1e-06, time_each_step=0.2s, eta=0:4:33\n",
      "2021-08-13 17:21:14 [INFO]\t[TRAIN] Epoch=82/100, Step=43/43, loss=9.832531, lr=1e-06, time_each_step=0.2s, eta=0:4:33\n",
      "2021-08-13 17:21:14 [INFO]\t[TRAIN] Epoch 82 finished, loss=10.567099, lr=1e-06 .\n",
      "2021-08-13 17:21:19 [INFO]\t[TRAIN] Epoch=83/100, Step=2/43, loss=10.078505, lr=1e-06, time_each_step=0.38s, eta=0:5:37\n",
      "2021-08-13 17:21:19 [INFO]\t[TRAIN] Epoch=83/100, Step=4/43, loss=7.297482, lr=1e-06, time_each_step=0.38s, eta=0:5:36\n",
      "2021-08-13 17:21:20 [INFO]\t[TRAIN] Epoch=83/100, Step=6/43, loss=6.456873, lr=1e-06, time_each_step=0.4s, eta=0:5:36\n",
      "2021-08-13 17:21:20 [INFO]\t[TRAIN] Epoch=83/100, Step=8/43, loss=13.51409, lr=1e-06, time_each_step=0.42s, eta=0:5:36\n",
      "2021-08-13 17:21:21 [INFO]\t[TRAIN] Epoch=83/100, Step=10/43, loss=17.467302, lr=1e-06, time_each_step=0.45s, eta=0:5:36\n",
      "2021-08-13 17:21:22 [INFO]\t[TRAIN] Epoch=83/100, Step=12/43, loss=8.609329, lr=1e-06, time_each_step=0.45s, eta=0:5:35\n",
      "2021-08-13 17:21:22 [INFO]\t[TRAIN] Epoch=83/100, Step=14/43, loss=9.506402, lr=1e-06, time_each_step=0.47s, eta=0:5:35\n",
      "2021-08-13 17:21:23 [INFO]\t[TRAIN] Epoch=83/100, Step=16/43, loss=10.287796, lr=1e-06, time_each_step=0.48s, eta=0:5:34\n",
      "2021-08-13 17:21:24 [INFO]\t[TRAIN] Epoch=83/100, Step=18/43, loss=10.525573, lr=1e-06, time_each_step=0.49s, eta=0:5:33\n",
      "2021-08-13 17:21:24 [INFO]\t[TRAIN] Epoch=83/100, Step=20/43, loss=14.870341, lr=1e-06, time_each_step=0.49s, eta=0:5:32\n",
      "2021-08-13 17:21:25 [INFO]\t[TRAIN] Epoch=83/100, Step=22/43, loss=10.966938, lr=1e-06, time_each_step=0.31s, eta=0:5:28\n",
      "2021-08-13 17:21:25 [INFO]\t[TRAIN] Epoch=83/100, Step=24/43, loss=5.830214, lr=1e-06, time_each_step=0.31s, eta=0:5:27\n",
      "2021-08-13 17:21:26 [INFO]\t[TRAIN] Epoch=83/100, Step=26/43, loss=11.358496, lr=1e-06, time_each_step=0.3s, eta=0:5:26\n",
      "2021-08-13 17:21:26 [INFO]\t[TRAIN] Epoch=83/100, Step=28/43, loss=6.006887, lr=1e-06, time_each_step=0.28s, eta=0:5:25\n",
      "2021-08-13 17:21:27 [INFO]\t[TRAIN] Epoch=83/100, Step=30/43, loss=8.217813, lr=1e-06, time_each_step=0.26s, eta=0:5:25\n",
      "2021-08-13 17:21:27 [INFO]\t[TRAIN] Epoch=83/100, Step=32/43, loss=8.144363, lr=1e-06, time_each_step=0.25s, eta=0:5:24\n",
      "2021-08-13 17:21:27 [INFO]\t[TRAIN] Epoch=83/100, Step=34/43, loss=16.665085, lr=1e-06, time_each_step=0.25s, eta=0:5:23\n",
      "2021-08-13 17:21:28 [INFO]\t[TRAIN] Epoch=83/100, Step=36/43, loss=13.653137, lr=1e-06, time_each_step=0.23s, eta=0:5:23\n",
      "2021-08-13 17:21:28 [INFO]\t[TRAIN] Epoch=83/100, Step=38/43, loss=10.724767, lr=1e-06, time_each_step=0.22s, eta=0:5:22\n",
      "2021-08-13 17:21:28 [INFO]\t[TRAIN] Epoch=83/100, Step=40/43, loss=7.824854, lr=1e-06, time_each_step=0.21s, eta=0:5:22\n",
      "2021-08-13 17:21:29 [INFO]\t[TRAIN] Epoch=83/100, Step=42/43, loss=8.985328, lr=1e-06, time_each_step=0.22s, eta=0:5:21\n",
      "2021-08-13 17:21:29 [INFO]\t[TRAIN] Epoch 83 finished, loss=10.983067, lr=1e-06 .\n",
      "2021-08-13 17:21:33 [INFO]\t[TRAIN] Epoch=84/100, Step=1/43, loss=6.592784, lr=1e-06, time_each_step=0.4s, eta=0:4:38\n",
      "2021-08-13 17:21:34 [INFO]\t[TRAIN] Epoch=84/100, Step=3/43, loss=11.541071, lr=1e-06, time_each_step=0.41s, eta=0:4:37\n",
      "2021-08-13 17:21:34 [INFO]\t[TRAIN] Epoch=84/100, Step=5/43, loss=11.878046, lr=1e-06, time_each_step=0.41s, eta=0:4:37\n",
      "2021-08-13 17:21:35 [INFO]\t[TRAIN] Epoch=84/100, Step=7/43, loss=9.301828, lr=1e-06, time_each_step=0.41s, eta=0:4:36\n",
      "2021-08-13 17:21:36 [INFO]\t[TRAIN] Epoch=84/100, Step=9/43, loss=11.857391, lr=1e-06, time_each_step=0.43s, eta=0:4:36\n",
      "2021-08-13 17:21:36 [INFO]\t[TRAIN] Epoch=84/100, Step=11/43, loss=8.403565, lr=1e-06, time_each_step=0.44s, eta=0:4:35\n",
      "2021-08-13 17:21:37 [INFO]\t[TRAIN] Epoch=84/100, Step=13/43, loss=8.846416, lr=1e-06, time_each_step=0.46s, eta=0:4:35\n",
      "2021-08-13 17:21:38 [INFO]\t[TRAIN] Epoch=84/100, Step=15/43, loss=9.557891, lr=1e-06, time_each_step=0.47s, eta=0:4:34\n",
      "2021-08-13 17:21:38 [INFO]\t[TRAIN] Epoch=84/100, Step=17/43, loss=10.132665, lr=1e-06, time_each_step=0.48s, eta=0:4:33\n",
      "2021-08-13 17:21:39 [INFO]\t[TRAIN] Epoch=84/100, Step=19/43, loss=11.981204, lr=1e-06, time_each_step=0.48s, eta=0:4:33\n",
      "2021-08-13 17:21:39 [INFO]\t[TRAIN] Epoch=84/100, Step=21/43, loss=5.994902, lr=1e-06, time_each_step=0.3s, eta=0:4:28\n",
      "2021-08-13 17:21:40 [INFO]\t[TRAIN] Epoch=84/100, Step=23/43, loss=11.399417, lr=1e-06, time_each_step=0.32s, eta=0:4:27\n",
      "2021-08-13 17:21:41 [INFO]\t[TRAIN] Epoch=84/100, Step=25/43, loss=7.928263, lr=1e-06, time_each_step=0.32s, eta=0:4:27\n",
      "2021-08-13 17:21:41 [INFO]\t[TRAIN] Epoch=84/100, Step=27/43, loss=6.573401, lr=1e-06, time_each_step=0.31s, eta=0:4:26\n",
      "2021-08-13 17:21:41 [INFO]\t[TRAIN] Epoch=84/100, Step=29/43, loss=6.412516, lr=1e-06, time_each_step=0.29s, eta=0:4:25\n",
      "2021-08-13 17:21:42 [INFO]\t[TRAIN] Epoch=84/100, Step=31/43, loss=11.761535, lr=1e-06, time_each_step=0.27s, eta=0:4:24\n",
      "2021-08-13 17:21:42 [INFO]\t[TRAIN] Epoch=84/100, Step=33/43, loss=11.647993, lr=1e-06, time_each_step=0.26s, eta=0:4:24\n",
      "2021-08-13 17:21:43 [INFO]\t[TRAIN] Epoch=84/100, Step=35/43, loss=10.266357, lr=1e-06, time_each_step=0.25s, eta=0:4:23\n",
      "2021-08-13 17:21:43 [INFO]\t[TRAIN] Epoch=84/100, Step=37/43, loss=11.667267, lr=1e-06, time_each_step=0.25s, eta=0:4:23\n",
      "2021-08-13 17:21:44 [INFO]\t[TRAIN] Epoch=84/100, Step=39/43, loss=13.87141, lr=1e-06, time_each_step=0.25s, eta=0:4:22\n",
      "2021-08-13 17:21:44 [INFO]\t[TRAIN] Epoch=84/100, Step=41/43, loss=8.704988, lr=1e-06, time_each_step=0.24s, eta=0:4:21\n",
      "2021-08-13 17:21:44 [INFO]\t[TRAIN] Epoch=84/100, Step=43/43, loss=14.278315, lr=1e-06, time_each_step=0.22s, eta=0:4:21\n",
      "2021-08-13 17:21:44 [INFO]\t[TRAIN] Epoch 84 finished, loss=10.196532, lr=1e-06 .\n",
      "2021-08-13 17:21:55 [INFO]\t[TRAIN] Epoch=85/100, Step=2/43, loss=7.580524, lr=1e-06, time_each_step=0.73s, eta=0:4:41\n",
      "2021-08-13 17:21:56 [INFO]\t[TRAIN] Epoch=85/100, Step=4/43, loss=11.135235, lr=1e-06, time_each_step=0.73s, eta=0:4:40\n",
      "2021-08-13 17:21:56 [INFO]\t[TRAIN] Epoch=85/100, Step=6/43, loss=9.830254, lr=1e-06, time_each_step=0.75s, eta=0:4:39\n",
      "2021-08-13 17:21:57 [INFO]\t[TRAIN] Epoch=85/100, Step=8/43, loss=11.2396, lr=1e-06, time_each_step=0.76s, eta=0:4:38\n",
      "2021-08-13 17:21:58 [INFO]\t[TRAIN] Epoch=85/100, Step=10/43, loss=12.048588, lr=1e-06, time_each_step=0.78s, eta=0:4:37\n",
      "2021-08-13 17:21:58 [INFO]\t[TRAIN] Epoch=85/100, Step=12/43, loss=10.880357, lr=1e-06, time_each_step=0.78s, eta=0:4:36\n",
      "2021-08-13 17:21:59 [INFO]\t[TRAIN] Epoch=85/100, Step=14/43, loss=6.54341, lr=1e-06, time_each_step=0.79s, eta=0:4:34\n",
      "2021-08-13 17:22:00 [INFO]\t[TRAIN] Epoch=85/100, Step=16/43, loss=7.398654, lr=1e-06, time_each_step=0.8s, eta=0:4:33\n",
      "2021-08-13 17:22:00 [INFO]\t[TRAIN] Epoch=85/100, Step=18/43, loss=13.469771, lr=1e-06, time_each_step=0.81s, eta=0:4:31\n",
      "2021-08-13 17:22:01 [INFO]\t[TRAIN] Epoch=85/100, Step=20/43, loss=13.429761, lr=1e-06, time_each_step=0.82s, eta=0:4:30\n",
      "2021-08-13 17:22:02 [INFO]\t[TRAIN] Epoch=85/100, Step=22/43, loss=10.43067, lr=1e-06, time_each_step=0.32s, eta=0:4:18\n",
      "2021-08-13 17:22:02 [INFO]\t[TRAIN] Epoch=85/100, Step=24/43, loss=10.809125, lr=1e-06, time_each_step=0.32s, eta=0:4:17\n",
      "2021-08-13 17:22:02 [INFO]\t[TRAIN] Epoch=85/100, Step=26/43, loss=8.146111, lr=1e-06, time_each_step=0.31s, eta=0:4:17\n",
      "2021-08-13 17:22:03 [INFO]\t[TRAIN] Epoch=85/100, Step=28/43, loss=13.465382, lr=1e-06, time_each_step=0.29s, eta=0:4:16\n",
      "2021-08-13 17:22:03 [INFO]\t[TRAIN] Epoch=85/100, Step=30/43, loss=10.290998, lr=1e-06, time_each_step=0.27s, eta=0:4:15\n",
      "2021-08-13 17:22:03 [INFO]\t[TRAIN] Epoch=85/100, Step=32/43, loss=10.259792, lr=1e-06, time_each_step=0.26s, eta=0:4:14\n",
      "2021-08-13 17:22:04 [INFO]\t[TRAIN] Epoch=85/100, Step=34/43, loss=8.957718, lr=1e-06, time_each_step=0.24s, eta=0:4:14\n",
      "2021-08-13 17:22:04 [INFO]\t[TRAIN] Epoch=85/100, Step=36/43, loss=11.81137, lr=1e-06, time_each_step=0.23s, eta=0:4:13\n",
      "2021-08-13 17:22:05 [INFO]\t[TRAIN] Epoch=85/100, Step=38/43, loss=9.84688, lr=1e-06, time_each_step=0.23s, eta=0:4:12\n",
      "2021-08-13 17:22:05 [INFO]\t[TRAIN] Epoch=85/100, Step=40/43, loss=9.499027, lr=1e-06, time_each_step=0.22s, eta=0:4:12\n",
      "2021-08-13 17:22:06 [INFO]\t[TRAIN] Epoch=85/100, Step=42/43, loss=15.464632, lr=1e-06, time_each_step=0.19s, eta=0:4:12\n",
      "2021-08-13 17:22:06 [INFO]\t[TRAIN] Epoch 85 finished, loss=10.544028, lr=1e-06 .\n",
      "2021-08-13 17:22:14 [INFO]\t[TRAIN] Epoch=86/100, Step=1/43, loss=9.648936, lr=1e-06, time_each_step=0.6s, eta=0:5:46\n",
      "2021-08-13 17:22:15 [INFO]\t[TRAIN] Epoch=86/100, Step=3/43, loss=6.737861, lr=1e-06, time_each_step=0.61s, eta=0:5:45\n",
      "2021-08-13 17:22:15 [INFO]\t[TRAIN] Epoch=86/100, Step=5/43, loss=16.443195, lr=1e-06, time_each_step=0.61s, eta=0:5:44\n",
      "2021-08-13 17:22:16 [INFO]\t[TRAIN] Epoch=86/100, Step=7/43, loss=15.125488, lr=1e-06, time_each_step=0.64s, eta=0:5:43\n",
      "2021-08-13 17:22:16 [INFO]\t[TRAIN] Epoch=86/100, Step=9/43, loss=17.913025, lr=1e-06, time_each_step=0.65s, eta=0:5:43\n",
      "2021-08-13 17:22:17 [INFO]\t[TRAIN] Epoch=86/100, Step=11/43, loss=11.314867, lr=1e-06, time_each_step=0.66s, eta=0:5:42\n",
      "2021-08-13 17:22:18 [INFO]\t[TRAIN] Epoch=86/100, Step=13/43, loss=8.116391, lr=1e-06, time_each_step=0.68s, eta=0:5:41\n",
      "2021-08-13 17:22:18 [INFO]\t[TRAIN] Epoch=86/100, Step=15/43, loss=9.547962, lr=1e-06, time_each_step=0.68s, eta=0:5:40\n",
      "2021-08-13 17:22:19 [INFO]\t[TRAIN] Epoch=86/100, Step=17/43, loss=13.185533, lr=1e-06, time_each_step=0.68s, eta=0:5:38\n",
      "2021-08-13 17:22:19 [INFO]\t[TRAIN] Epoch=86/100, Step=19/43, loss=9.777273, lr=1e-06, time_each_step=0.69s, eta=0:5:37\n",
      "2021-08-13 17:22:20 [INFO]\t[TRAIN] Epoch=86/100, Step=21/43, loss=10.969805, lr=1e-06, time_each_step=0.29s, eta=0:5:27\n",
      "2021-08-13 17:22:20 [INFO]\t[TRAIN] Epoch=86/100, Step=23/43, loss=6.556603, lr=1e-06, time_each_step=0.28s, eta=0:5:26\n",
      "2021-08-13 17:22:21 [INFO]\t[TRAIN] Epoch=86/100, Step=25/43, loss=9.072831, lr=1e-06, time_each_step=0.29s, eta=0:5:26\n",
      "2021-08-13 17:22:21 [INFO]\t[TRAIN] Epoch=86/100, Step=27/43, loss=9.682229, lr=1e-06, time_each_step=0.28s, eta=0:5:25\n",
      "2021-08-13 17:22:22 [INFO]\t[TRAIN] Epoch=86/100, Step=29/43, loss=13.43553, lr=1e-06, time_each_step=0.27s, eta=0:5:24\n",
      "2021-08-13 17:22:22 [INFO]\t[TRAIN] Epoch=86/100, Step=31/43, loss=12.99354, lr=1e-06, time_each_step=0.27s, eta=0:5:24\n",
      "2021-08-13 17:22:23 [INFO]\t[TRAIN] Epoch=86/100, Step=33/43, loss=9.583923, lr=1e-06, time_each_step=0.24s, eta=0:5:23\n",
      "2021-08-13 17:22:23 [INFO]\t[TRAIN] Epoch=86/100, Step=35/43, loss=7.762841, lr=1e-06, time_each_step=0.22s, eta=0:5:22\n",
      "2021-08-13 17:22:23 [INFO]\t[TRAIN] Epoch=86/100, Step=37/43, loss=11.465706, lr=1e-06, time_each_step=0.22s, eta=0:5:22\n",
      "2021-08-13 17:22:24 [INFO]\t[TRAIN] Epoch=86/100, Step=39/43, loss=6.717563, lr=1e-06, time_each_step=0.21s, eta=0:5:21\n",
      "2021-08-13 17:22:24 [INFO]\t[TRAIN] Epoch=86/100, Step=41/43, loss=9.941358, lr=1e-06, time_each_step=0.21s, eta=0:5:21\n",
      "2021-08-13 17:22:24 [INFO]\t[TRAIN] Epoch=86/100, Step=43/43, loss=8.794482, lr=1e-06, time_each_step=0.21s, eta=0:5:21\n",
      "2021-08-13 17:22:24 [INFO]\t[TRAIN] Epoch 86 finished, loss=10.666056, lr=1e-06 .\n",
      "2021-08-13 17:22:29 [INFO]\t[TRAIN] Epoch=87/100, Step=2/43, loss=9.017641, lr=1e-06, time_each_step=0.4s, eta=0:4:43\n",
      "2021-08-13 17:22:29 [INFO]\t[TRAIN] Epoch=87/100, Step=4/43, loss=11.211522, lr=1e-06, time_each_step=0.4s, eta=0:4:42\n",
      "2021-08-13 17:22:30 [INFO]\t[TRAIN] Epoch=87/100, Step=6/43, loss=7.964156, lr=1e-06, time_each_step=0.41s, eta=0:4:42\n",
      "2021-08-13 17:22:30 [INFO]\t[TRAIN] Epoch=87/100, Step=8/43, loss=9.667729, lr=1e-06, time_each_step=0.41s, eta=0:4:41\n",
      "2021-08-13 17:22:31 [INFO]\t[TRAIN] Epoch=87/100, Step=10/43, loss=7.563977, lr=1e-06, time_each_step=0.43s, eta=0:4:41\n",
      "2021-08-13 17:22:32 [INFO]\t[TRAIN] Epoch=87/100, Step=12/43, loss=9.124381, lr=1e-06, time_each_step=0.44s, eta=0:4:41\n",
      "2021-08-13 17:22:32 [INFO]\t[TRAIN] Epoch=87/100, Step=14/43, loss=9.320495, lr=1e-06, time_each_step=0.47s, eta=0:4:40\n",
      "2021-08-13 17:22:33 [INFO]\t[TRAIN] Epoch=87/100, Step=16/43, loss=9.5084, lr=1e-06, time_each_step=0.48s, eta=0:4:40\n",
      "2021-08-13 17:22:34 [INFO]\t[TRAIN] Epoch=87/100, Step=18/43, loss=8.612517, lr=1e-06, time_each_step=0.48s, eta=0:4:39\n",
      "2021-08-13 17:22:34 [INFO]\t[TRAIN] Epoch=87/100, Step=20/43, loss=10.205557, lr=1e-06, time_each_step=0.48s, eta=0:4:38\n",
      "2021-08-13 17:22:35 [INFO]\t[TRAIN] Epoch=87/100, Step=22/43, loss=14.091356, lr=1e-06, time_each_step=0.3s, eta=0:4:33\n",
      "2021-08-13 17:22:35 [INFO]\t[TRAIN] Epoch=87/100, Step=24/43, loss=10.006008, lr=1e-06, time_each_step=0.31s, eta=0:4:33\n",
      "2021-08-13 17:22:36 [INFO]\t[TRAIN] Epoch=87/100, Step=26/43, loss=14.085835, lr=1e-06, time_each_step=0.29s, eta=0:4:32\n",
      "2021-08-13 17:22:36 [INFO]\t[TRAIN] Epoch=87/100, Step=28/43, loss=9.015037, lr=1e-06, time_each_step=0.29s, eta=0:4:31\n",
      "2021-08-13 17:22:37 [INFO]\t[TRAIN] Epoch=87/100, Step=30/43, loss=15.980921, lr=1e-06, time_each_step=0.28s, eta=0:4:31\n",
      "2021-08-13 17:22:37 [INFO]\t[TRAIN] Epoch=87/100, Step=32/43, loss=10.355455, lr=1e-06, time_each_step=0.28s, eta=0:4:30\n",
      "2021-08-13 17:22:38 [INFO]\t[TRAIN] Epoch=87/100, Step=34/43, loss=9.889294, lr=1e-06, time_each_step=0.25s, eta=0:4:29\n",
      "2021-08-13 17:22:38 [INFO]\t[TRAIN] Epoch=87/100, Step=36/43, loss=11.620268, lr=1e-06, time_each_step=0.25s, eta=0:4:29\n",
      "2021-08-13 17:22:39 [INFO]\t[TRAIN] Epoch=87/100, Step=38/43, loss=10.221796, lr=1e-06, time_each_step=0.24s, eta=0:4:28\n",
      "2021-08-13 17:22:39 [INFO]\t[TRAIN] Epoch=87/100, Step=40/43, loss=10.236396, lr=1e-06, time_each_step=0.24s, eta=0:4:28\n",
      "2021-08-13 17:22:39 [INFO]\t[TRAIN] Epoch=87/100, Step=42/43, loss=11.332644, lr=1e-06, time_each_step=0.21s, eta=0:4:27\n",
      "2021-08-13 17:22:39 [INFO]\t[TRAIN] Epoch 87 finished, loss=10.550254, lr=1e-06 .\n",
      "2021-08-13 17:22:44 [INFO]\t[TRAIN] Epoch=88/100, Step=1/43, loss=8.213278, lr=1e-06, time_each_step=0.4s, eta=0:3:41\n",
      "2021-08-13 17:22:44 [INFO]\t[TRAIN] Epoch=88/100, Step=3/43, loss=11.881956, lr=1e-06, time_each_step=0.4s, eta=0:3:40\n",
      "2021-08-13 17:22:45 [INFO]\t[TRAIN] Epoch=88/100, Step=5/43, loss=9.056669, lr=1e-06, time_each_step=0.42s, eta=0:3:40\n",
      "2021-08-13 17:22:45 [INFO]\t[TRAIN] Epoch=88/100, Step=7/43, loss=9.533433, lr=1e-06, time_each_step=0.43s, eta=0:3:39\n",
      "2021-08-13 17:22:46 [INFO]\t[TRAIN] Epoch=88/100, Step=9/43, loss=15.51074, lr=1e-06, time_each_step=0.43s, eta=0:3:39\n",
      "2021-08-13 17:22:46 [INFO]\t[TRAIN] Epoch=88/100, Step=11/43, loss=6.882227, lr=1e-06, time_each_step=0.44s, eta=0:3:38\n",
      "2021-08-13 17:22:47 [INFO]\t[TRAIN] Epoch=88/100, Step=13/43, loss=9.425143, lr=1e-06, time_each_step=0.45s, eta=0:3:37\n",
      "2021-08-13 17:22:48 [INFO]\t[TRAIN] Epoch=88/100, Step=15/43, loss=8.199998, lr=1e-06, time_each_step=0.47s, eta=0:3:37\n",
      "2021-08-13 17:22:49 [INFO]\t[TRAIN] Epoch=88/100, Step=17/43, loss=11.314833, lr=1e-06, time_each_step=0.49s, eta=0:3:37\n",
      "2021-08-13 17:22:49 [INFO]\t[TRAIN] Epoch=88/100, Step=19/43, loss=10.8557, lr=1e-06, time_each_step=0.51s, eta=0:3:36\n",
      "2021-08-13 17:22:50 [INFO]\t[TRAIN] Epoch=88/100, Step=21/43, loss=10.811062, lr=1e-06, time_each_step=0.31s, eta=0:3:31\n",
      "2021-08-13 17:22:50 [INFO]\t[TRAIN] Epoch=88/100, Step=23/43, loss=11.416154, lr=1e-06, time_each_step=0.31s, eta=0:3:30\n",
      "2021-08-13 17:22:50 [INFO]\t[TRAIN] Epoch=88/100, Step=25/43, loss=10.002032, lr=1e-06, time_each_step=0.28s, eta=0:3:29\n",
      "2021-08-13 17:22:51 [INFO]\t[TRAIN] Epoch=88/100, Step=27/43, loss=11.27962, lr=1e-06, time_each_step=0.28s, eta=0:3:28\n",
      "2021-08-13 17:22:51 [INFO]\t[TRAIN] Epoch=88/100, Step=29/43, loss=14.953671, lr=1e-06, time_each_step=0.28s, eta=0:3:28\n",
      "2021-08-13 17:22:52 [INFO]\t[TRAIN] Epoch=88/100, Step=31/43, loss=10.483125, lr=1e-06, time_each_step=0.27s, eta=0:3:27\n",
      "2021-08-13 17:22:52 [INFO]\t[TRAIN] Epoch=88/100, Step=33/43, loss=7.868788, lr=1e-06, time_each_step=0.26s, eta=0:3:26\n",
      "2021-08-13 17:22:53 [INFO]\t[TRAIN] Epoch=88/100, Step=35/43, loss=10.321684, lr=1e-06, time_each_step=0.25s, eta=0:3:26\n",
      "2021-08-13 17:22:53 [INFO]\t[TRAIN] Epoch=88/100, Step=37/43, loss=9.614367, lr=1e-06, time_each_step=0.23s, eta=0:3:25\n",
      "2021-08-13 17:22:54 [INFO]\t[TRAIN] Epoch=88/100, Step=39/43, loss=8.740711, lr=1e-06, time_each_step=0.21s, eta=0:3:25\n",
      "2021-08-13 17:22:54 [INFO]\t[TRAIN] Epoch=88/100, Step=41/43, loss=9.717289, lr=1e-06, time_each_step=0.22s, eta=0:3:24\n",
      "2021-08-13 17:22:55 [INFO]\t[TRAIN] Epoch=88/100, Step=43/43, loss=6.171633, lr=1e-06, time_each_step=0.24s, eta=0:3:24\n",
      "2021-08-13 17:22:55 [INFO]\t[TRAIN] Epoch 88 finished, loss=9.846425, lr=1e-06 .\n",
      "2021-08-13 17:23:00 [INFO]\t[TRAIN] Epoch=89/100, Step=2/43, loss=9.638191, lr=1e-06, time_each_step=0.5s, eta=0:3:32\n",
      "2021-08-13 17:23:01 [INFO]\t[TRAIN] Epoch=89/100, Step=4/43, loss=9.604254, lr=1e-06, time_each_step=0.5s, eta=0:3:31\n",
      "2021-08-13 17:23:02 [INFO]\t[TRAIN] Epoch=89/100, Step=6/43, loss=13.695936, lr=1e-06, time_each_step=0.51s, eta=0:3:31\n",
      "2021-08-13 17:23:02 [INFO]\t[TRAIN] Epoch=89/100, Step=8/43, loss=7.637119, lr=1e-06, time_each_step=0.52s, eta=0:3:30\n",
      "2021-08-13 17:23:03 [INFO]\t[TRAIN] Epoch=89/100, Step=10/43, loss=8.888483, lr=1e-06, time_each_step=0.52s, eta=0:3:29\n",
      "2021-08-13 17:23:03 [INFO]\t[TRAIN] Epoch=89/100, Step=12/43, loss=10.130761, lr=1e-06, time_each_step=0.51s, eta=0:3:28\n",
      "2021-08-13 17:23:04 [INFO]\t[TRAIN] Epoch=89/100, Step=14/43, loss=9.001264, lr=1e-06, time_each_step=0.53s, eta=0:3:27\n",
      "2021-08-13 17:23:04 [INFO]\t[TRAIN] Epoch=89/100, Step=16/43, loss=14.659567, lr=1e-06, time_each_step=0.54s, eta=0:3:27\n",
      "2021-08-13 17:23:05 [INFO]\t[TRAIN] Epoch=89/100, Step=18/43, loss=9.294359, lr=1e-06, time_each_step=0.54s, eta=0:3:26\n",
      "2021-08-13 17:23:06 [INFO]\t[TRAIN] Epoch=89/100, Step=20/43, loss=12.056786, lr=1e-06, time_each_step=0.54s, eta=0:3:24\n",
      "2021-08-13 17:23:06 [INFO]\t[TRAIN] Epoch=89/100, Step=22/43, loss=11.752702, lr=1e-06, time_each_step=0.28s, eta=0:3:18\n",
      "2021-08-13 17:23:06 [INFO]\t[TRAIN] Epoch=89/100, Step=24/43, loss=9.301443, lr=1e-06, time_each_step=0.28s, eta=0:3:17\n",
      "2021-08-13 17:23:07 [INFO]\t[TRAIN] Epoch=89/100, Step=26/43, loss=8.483609, lr=1e-06, time_each_step=0.26s, eta=0:3:16\n",
      "2021-08-13 17:23:07 [INFO]\t[TRAIN] Epoch=89/100, Step=28/43, loss=10.593306, lr=1e-06, time_each_step=0.26s, eta=0:3:16\n",
      "2021-08-13 17:23:08 [INFO]\t[TRAIN] Epoch=89/100, Step=30/43, loss=10.168211, lr=1e-06, time_each_step=0.26s, eta=0:3:15\n",
      "2021-08-13 17:23:08 [INFO]\t[TRAIN] Epoch=89/100, Step=32/43, loss=8.445275, lr=1e-06, time_each_step=0.24s, eta=0:3:15\n",
      "2021-08-13 17:23:08 [INFO]\t[TRAIN] Epoch=89/100, Step=34/43, loss=11.731464, lr=1e-06, time_each_step=0.23s, eta=0:3:14\n",
      "2021-08-13 17:23:09 [INFO]\t[TRAIN] Epoch=89/100, Step=36/43, loss=7.54064, lr=1e-06, time_each_step=0.22s, eta=0:3:14\n",
      "2021-08-13 17:23:09 [INFO]\t[TRAIN] Epoch=89/100, Step=38/43, loss=9.979648, lr=1e-06, time_each_step=0.2s, eta=0:3:13\n",
      "2021-08-13 17:23:09 [INFO]\t[TRAIN] Epoch=89/100, Step=40/43, loss=15.971941, lr=1e-06, time_each_step=0.19s, eta=0:3:13\n",
      "2021-08-13 17:23:10 [INFO]\t[TRAIN] Epoch=89/100, Step=42/43, loss=10.667818, lr=1e-06, time_each_step=0.19s, eta=0:3:12\n",
      "2021-08-13 17:23:10 [INFO]\t[TRAIN] Epoch 89 finished, loss=10.27511, lr=1e-06 .\n",
      "2021-08-13 17:23:14 [INFO]\t[TRAIN] Epoch=90/100, Step=1/43, loss=11.384399, lr=1e-06, time_each_step=0.36s, eta=0:3:10\n",
      "2021-08-13 17:23:14 [INFO]\t[TRAIN] Epoch=90/100, Step=3/43, loss=7.895762, lr=1e-06, time_each_step=0.36s, eta=0:3:9\n",
      "2021-08-13 17:23:15 [INFO]\t[TRAIN] Epoch=90/100, Step=5/43, loss=13.096055, lr=1e-06, time_each_step=0.37s, eta=0:3:9\n",
      "2021-08-13 17:23:15 [INFO]\t[TRAIN] Epoch=90/100, Step=7/43, loss=7.048049, lr=1e-06, time_each_step=0.36s, eta=0:3:8\n",
      "2021-08-13 17:23:16 [INFO]\t[TRAIN] Epoch=90/100, Step=9/43, loss=12.155548, lr=1e-06, time_each_step=0.39s, eta=0:3:8\n",
      "2021-08-13 17:23:16 [INFO]\t[TRAIN] Epoch=90/100, Step=11/43, loss=7.769754, lr=1e-06, time_each_step=0.39s, eta=0:3:7\n",
      "2021-08-13 17:23:17 [INFO]\t[TRAIN] Epoch=90/100, Step=13/43, loss=9.014095, lr=1e-06, time_each_step=0.41s, eta=0:3:7\n",
      "2021-08-13 17:23:18 [INFO]\t[TRAIN] Epoch=90/100, Step=15/43, loss=15.147617, lr=1e-06, time_each_step=0.42s, eta=0:3:7\n",
      "2021-08-13 17:23:18 [INFO]\t[TRAIN] Epoch=90/100, Step=17/43, loss=13.745119, lr=1e-06, time_each_step=0.43s, eta=0:3:6\n",
      "2021-08-13 17:23:19 [INFO]\t[TRAIN] Epoch=90/100, Step=19/43, loss=7.999924, lr=1e-06, time_each_step=0.45s, eta=0:3:5\n",
      "2021-08-13 17:23:19 [INFO]\t[TRAIN] Epoch=90/100, Step=21/43, loss=11.7417, lr=1e-06, time_each_step=0.28s, eta=0:3:1\n",
      "2021-08-13 17:23:20 [INFO]\t[TRAIN] Epoch=90/100, Step=23/43, loss=11.963381, lr=1e-06, time_each_step=0.29s, eta=0:3:0\n",
      "2021-08-13 17:23:20 [INFO]\t[TRAIN] Epoch=90/100, Step=25/43, loss=10.627728, lr=1e-06, time_each_step=0.27s, eta=0:2:59\n",
      "2021-08-13 17:23:20 [INFO]\t[TRAIN] Epoch=90/100, Step=27/43, loss=11.796064, lr=1e-06, time_each_step=0.26s, eta=0:2:59\n",
      "2021-08-13 17:23:21 [INFO]\t[TRAIN] Epoch=90/100, Step=29/43, loss=8.243355, lr=1e-06, time_each_step=0.25s, eta=0:2:58\n",
      "2021-08-13 17:23:21 [INFO]\t[TRAIN] Epoch=90/100, Step=31/43, loss=7.841391, lr=1e-06, time_each_step=0.24s, eta=0:2:58\n",
      "2021-08-13 17:23:22 [INFO]\t[TRAIN] Epoch=90/100, Step=33/43, loss=8.115192, lr=1e-06, time_each_step=0.24s, eta=0:2:57\n",
      "2021-08-13 17:23:22 [INFO]\t[TRAIN] Epoch=90/100, Step=35/43, loss=6.739198, lr=1e-06, time_each_step=0.22s, eta=0:2:56\n",
      "2021-08-13 17:23:22 [INFO]\t[TRAIN] Epoch=90/100, Step=37/43, loss=10.39304, lr=1e-06, time_each_step=0.22s, eta=0:2:56\n",
      "2021-08-13 17:23:23 [INFO]\t[TRAIN] Epoch=90/100, Step=39/43, loss=14.184817, lr=1e-06, time_each_step=0.21s, eta=0:2:55\n",
      "2021-08-13 17:23:23 [INFO]\t[TRAIN] Epoch=90/100, Step=41/43, loss=8.360415, lr=1e-06, time_each_step=0.21s, eta=0:2:55\n",
      "2021-08-13 17:23:24 [INFO]\t[TRAIN] Epoch=90/100, Step=43/43, loss=10.007035, lr=1e-06, time_each_step=0.21s, eta=0:2:55\n",
      "2021-08-13 17:23:24 [INFO]\t[TRAIN] Epoch 90 finished, loss=10.338325, lr=1e-06 .\n",
      "2021-08-13 17:23:24 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:06<00:00,  2.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:23:30 [INFO]\t[EVAL] Finished, Epoch=90, bbox_map=62.966123 .\n",
      "2021-08-13 17:23:31 [INFO]\tModel saved in output/yolov3_darknet53/epoch_90.\n",
      "2021-08-13 17:23:31 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_80, bbox_map=63.75984622249753\n",
      "2021-08-13 17:23:49 [INFO]\t[TRAIN] Epoch=91/100, Step=2/43, loss=11.02973, lr=1e-06, time_each_step=1.08s, eta=0:2:57\n",
      "2021-08-13 17:23:50 [INFO]\t[TRAIN] Epoch=91/100, Step=4/43, loss=8.43437, lr=1e-06, time_each_step=1.08s, eta=0:2:55\n",
      "2021-08-13 17:23:50 [INFO]\t[TRAIN] Epoch=91/100, Step=6/43, loss=8.712286, lr=1e-06, time_each_step=1.09s, eta=0:2:53\n",
      "2021-08-13 17:23:51 [INFO]\t[TRAIN] Epoch=91/100, Step=8/43, loss=13.695086, lr=1e-06, time_each_step=1.09s, eta=0:2:51\n",
      "2021-08-13 17:23:51 [INFO]\t[TRAIN] Epoch=91/100, Step=10/43, loss=11.008059, lr=1e-06, time_each_step=1.1s, eta=0:2:49\n",
      "2021-08-13 17:23:52 [INFO]\t[TRAIN] Epoch=91/100, Step=12/43, loss=11.387782, lr=1e-06, time_each_step=1.13s, eta=0:2:47\n",
      "2021-08-13 17:23:53 [INFO]\t[TRAIN] Epoch=91/100, Step=14/43, loss=9.944481, lr=1e-06, time_each_step=1.13s, eta=0:2:45\n",
      "2021-08-13 17:23:53 [INFO]\t[TRAIN] Epoch=91/100, Step=16/43, loss=7.919058, lr=1e-06, time_each_step=1.13s, eta=0:2:43\n",
      "2021-08-13 17:23:54 [INFO]\t[TRAIN] Epoch=91/100, Step=18/43, loss=15.047638, lr=1e-06, time_each_step=1.13s, eta=0:2:41\n",
      "2021-08-13 17:23:54 [INFO]\t[TRAIN] Epoch=91/100, Step=20/43, loss=8.845031, lr=1e-06, time_each_step=1.14s, eta=0:2:39\n",
      "2021-08-13 17:23:55 [INFO]\t[TRAIN] Epoch=91/100, Step=22/43, loss=8.852903, lr=1e-06, time_each_step=0.28s, eta=0:2:18\n",
      "2021-08-13 17:23:55 [INFO]\t[TRAIN] Epoch=91/100, Step=24/43, loss=13.632484, lr=1e-06, time_each_step=0.28s, eta=0:2:18\n",
      "2021-08-13 17:23:56 [INFO]\t[TRAIN] Epoch=91/100, Step=26/43, loss=7.318698, lr=1e-06, time_each_step=0.28s, eta=0:2:17\n",
      "2021-08-13 17:23:56 [INFO]\t[TRAIN] Epoch=91/100, Step=28/43, loss=9.022055, lr=1e-06, time_each_step=0.28s, eta=0:2:17\n",
      "2021-08-13 17:23:57 [INFO]\t[TRAIN] Epoch=91/100, Step=30/43, loss=7.880338, lr=1e-06, time_each_step=0.26s, eta=0:2:16\n",
      "2021-08-13 17:23:57 [INFO]\t[TRAIN] Epoch=91/100, Step=32/43, loss=14.809687, lr=1e-06, time_each_step=0.25s, eta=0:2:15\n",
      "2021-08-13 17:23:58 [INFO]\t[TRAIN] Epoch=91/100, Step=34/43, loss=10.277214, lr=1e-06, time_each_step=0.24s, eta=0:2:15\n",
      "2021-08-13 17:23:58 [INFO]\t[TRAIN] Epoch=91/100, Step=36/43, loss=9.123871, lr=1e-06, time_each_step=0.24s, eta=0:2:14\n",
      "2021-08-13 17:23:58 [INFO]\t[TRAIN] Epoch=91/100, Step=38/43, loss=14.370787, lr=1e-06, time_each_step=0.24s, eta=0:2:14\n",
      "2021-08-13 17:23:59 [INFO]\t[TRAIN] Epoch=91/100, Step=40/43, loss=10.189435, lr=1e-06, time_each_step=0.23s, eta=0:2:13\n",
      "2021-08-13 17:23:59 [INFO]\t[TRAIN] Epoch=91/100, Step=42/43, loss=13.194256, lr=1e-06, time_each_step=0.23s, eta=0:2:13\n",
      "2021-08-13 17:24:00 [INFO]\t[TRAIN] Epoch 91 finished, loss=10.368587, lr=1e-06 .\n",
      "2021-08-13 17:24:05 [INFO]\t[TRAIN] Epoch=92/100, Step=1/43, loss=9.09979, lr=1e-06, time_each_step=0.49s, eta=0:4:13\n",
      "2021-08-13 17:24:06 [INFO]\t[TRAIN] Epoch=92/100, Step=3/43, loss=8.775863, lr=1e-06, time_each_step=0.5s, eta=0:4:12\n",
      "2021-08-13 17:24:06 [INFO]\t[TRAIN] Epoch=92/100, Step=5/43, loss=12.804314, lr=1e-06, time_each_step=0.49s, eta=0:4:11\n",
      "2021-08-13 17:24:07 [INFO]\t[TRAIN] Epoch=92/100, Step=7/43, loss=8.355062, lr=1e-06, time_each_step=0.51s, eta=0:4:11\n",
      "2021-08-13 17:24:07 [INFO]\t[TRAIN] Epoch=92/100, Step=9/43, loss=11.582523, lr=1e-06, time_each_step=0.52s, eta=0:4:10\n",
      "2021-08-13 17:24:08 [INFO]\t[TRAIN] Epoch=92/100, Step=11/43, loss=20.994526, lr=1e-06, time_each_step=0.52s, eta=0:4:9\n",
      "2021-08-13 17:24:08 [INFO]\t[TRAIN] Epoch=92/100, Step=13/43, loss=9.789944, lr=1e-06, time_each_step=0.52s, eta=0:4:8\n",
      "2021-08-13 17:24:09 [INFO]\t[TRAIN] Epoch=92/100, Step=15/43, loss=8.323803, lr=1e-06, time_each_step=0.53s, eta=0:4:7\n",
      "2021-08-13 17:24:09 [INFO]\t[TRAIN] Epoch=92/100, Step=17/43, loss=8.530857, lr=1e-06, time_each_step=0.54s, eta=0:4:6\n",
      "2021-08-13 17:24:10 [INFO]\t[TRAIN] Epoch=92/100, Step=19/43, loss=6.598486, lr=1e-06, time_each_step=0.53s, eta=0:4:5\n",
      "2021-08-13 17:24:11 [INFO]\t[TRAIN] Epoch=92/100, Step=21/43, loss=12.206709, lr=1e-06, time_each_step=0.28s, eta=0:3:58\n",
      "2021-08-13 17:24:11 [INFO]\t[TRAIN] Epoch=92/100, Step=23/43, loss=8.654672, lr=1e-06, time_each_step=0.28s, eta=0:3:58\n",
      "2021-08-13 17:24:12 [INFO]\t[TRAIN] Epoch=92/100, Step=25/43, loss=9.209628, lr=1e-06, time_each_step=0.28s, eta=0:3:57\n",
      "2021-08-13 17:24:12 [INFO]\t[TRAIN] Epoch=92/100, Step=27/43, loss=16.545099, lr=1e-06, time_each_step=0.26s, eta=0:3:56\n",
      "2021-08-13 17:24:12 [INFO]\t[TRAIN] Epoch=92/100, Step=29/43, loss=9.101438, lr=1e-06, time_each_step=0.25s, eta=0:3:56\n",
      "2021-08-13 17:24:13 [INFO]\t[TRAIN] Epoch=92/100, Step=31/43, loss=10.132377, lr=1e-06, time_each_step=0.24s, eta=0:3:55\n",
      "2021-08-13 17:24:13 [INFO]\t[TRAIN] Epoch=92/100, Step=33/43, loss=8.007635, lr=1e-06, time_each_step=0.24s, eta=0:3:55\n",
      "2021-08-13 17:24:14 [INFO]\t[TRAIN] Epoch=92/100, Step=35/43, loss=8.119356, lr=1e-06, time_each_step=0.23s, eta=0:3:54\n",
      "2021-08-13 17:24:14 [INFO]\t[TRAIN] Epoch=92/100, Step=37/43, loss=9.024943, lr=1e-06, time_each_step=0.23s, eta=0:3:54\n",
      "2021-08-13 17:24:15 [INFO]\t[TRAIN] Epoch=92/100, Step=39/43, loss=13.577065, lr=1e-06, time_each_step=0.22s, eta=0:3:53\n",
      "2021-08-13 17:24:15 [INFO]\t[TRAIN] Epoch=92/100, Step=41/43, loss=10.337068, lr=1e-06, time_each_step=0.21s, eta=0:3:53\n",
      "2021-08-13 17:24:15 [INFO]\t[TRAIN] Epoch=92/100, Step=43/43, loss=9.363361, lr=1e-06, time_each_step=0.2s, eta=0:3:52\n",
      "2021-08-13 17:24:15 [INFO]\t[TRAIN] Epoch 92 finished, loss=10.384348, lr=1e-06 .\n",
      "2021-08-13 17:24:23 [INFO]\t[TRAIN] Epoch=93/100, Step=2/43, loss=9.942699, lr=1e-06, time_each_step=0.55s, eta=0:2:19\n",
      "2021-08-13 17:24:23 [INFO]\t[TRAIN] Epoch=93/100, Step=4/43, loss=5.707369, lr=1e-06, time_each_step=0.56s, eta=0:2:18\n",
      "2021-08-13 17:24:24 [INFO]\t[TRAIN] Epoch=93/100, Step=6/43, loss=10.790817, lr=1e-06, time_each_step=0.58s, eta=0:2:18\n",
      "2021-08-13 17:24:25 [INFO]\t[TRAIN] Epoch=93/100, Step=8/43, loss=8.425365, lr=1e-06, time_each_step=0.59s, eta=0:2:17\n",
      "2021-08-13 17:24:25 [INFO]\t[TRAIN] Epoch=93/100, Step=10/43, loss=8.648837, lr=1e-06, time_each_step=0.6s, eta=0:2:17\n",
      "2021-08-13 17:24:26 [INFO]\t[TRAIN] Epoch=93/100, Step=12/43, loss=8.176603, lr=1e-06, time_each_step=0.62s, eta=0:2:16\n",
      "2021-08-13 17:24:26 [INFO]\t[TRAIN] Epoch=93/100, Step=14/43, loss=9.019669, lr=1e-06, time_each_step=0.61s, eta=0:2:14\n",
      "2021-08-13 17:24:27 [INFO]\t[TRAIN] Epoch=93/100, Step=16/43, loss=15.314643, lr=1e-06, time_each_step=0.62s, eta=0:2:13\n",
      "2021-08-13 17:24:28 [INFO]\t[TRAIN] Epoch=93/100, Step=18/43, loss=11.154558, lr=1e-06, time_each_step=0.64s, eta=0:2:13\n",
      "2021-08-13 17:24:29 [INFO]\t[TRAIN] Epoch=93/100, Step=20/43, loss=10.332041, lr=1e-06, time_each_step=0.67s, eta=0:2:12\n",
      "2021-08-13 17:24:29 [INFO]\t[TRAIN] Epoch=93/100, Step=22/43, loss=10.013674, lr=1e-06, time_each_step=0.31s, eta=0:2:3\n",
      "2021-08-13 17:24:29 [INFO]\t[TRAIN] Epoch=93/100, Step=24/43, loss=8.843707, lr=1e-06, time_each_step=0.31s, eta=0:2:3\n",
      "2021-08-13 17:24:30 [INFO]\t[TRAIN] Epoch=93/100, Step=26/43, loss=9.636534, lr=1e-06, time_each_step=0.29s, eta=0:2:2\n",
      "2021-08-13 17:24:31 [INFO]\t[TRAIN] Epoch=93/100, Step=28/43, loss=10.234807, lr=1e-06, time_each_step=0.3s, eta=0:2:1\n",
      "2021-08-13 17:24:31 [INFO]\t[TRAIN] Epoch=93/100, Step=30/43, loss=12.271961, lr=1e-06, time_each_step=0.28s, eta=0:2:0\n",
      "2021-08-13 17:24:31 [INFO]\t[TRAIN] Epoch=93/100, Step=32/43, loss=12.308268, lr=1e-06, time_each_step=0.27s, eta=0:2:0\n",
      "2021-08-13 17:24:32 [INFO]\t[TRAIN] Epoch=93/100, Step=34/43, loss=8.107353, lr=1e-06, time_each_step=0.27s, eta=0:1:59\n",
      "2021-08-13 17:24:32 [INFO]\t[TRAIN] Epoch=93/100, Step=36/43, loss=10.032966, lr=1e-06, time_each_step=0.27s, eta=0:1:59\n",
      "2021-08-13 17:24:33 [INFO]\t[TRAIN] Epoch=93/100, Step=38/43, loss=5.124432, lr=1e-06, time_each_step=0.24s, eta=0:1:58\n",
      "2021-08-13 17:24:33 [INFO]\t[TRAIN] Epoch=93/100, Step=40/43, loss=16.051998, lr=1e-06, time_each_step=0.23s, eta=0:1:57\n",
      "2021-08-13 17:24:33 [INFO]\t[TRAIN] Epoch=93/100, Step=42/43, loss=7.477008, lr=1e-06, time_each_step=0.21s, eta=0:1:57\n",
      "2021-08-13 17:24:34 [INFO]\t[TRAIN] Epoch 93 finished, loss=10.104027, lr=1e-06 .\n",
      "2021-08-13 17:24:38 [INFO]\t[TRAIN] Epoch=94/100, Step=1/43, loss=8.559669, lr=1e-06, time_each_step=0.42s, eta=0:2:16\n",
      "2021-08-13 17:24:38 [INFO]\t[TRAIN] Epoch=94/100, Step=3/43, loss=7.88016, lr=1e-06, time_each_step=0.42s, eta=0:2:15\n",
      "2021-08-13 17:24:39 [INFO]\t[TRAIN] Epoch=94/100, Step=5/43, loss=13.403326, lr=1e-06, time_each_step=0.42s, eta=0:2:14\n",
      "2021-08-13 17:24:40 [INFO]\t[TRAIN] Epoch=94/100, Step=7/43, loss=12.231529, lr=1e-06, time_each_step=0.44s, eta=0:2:14\n",
      "2021-08-13 17:24:41 [INFO]\t[TRAIN] Epoch=94/100, Step=9/43, loss=12.57936, lr=1e-06, time_each_step=0.46s, eta=0:2:13\n",
      "2021-08-13 17:24:41 [INFO]\t[TRAIN] Epoch=94/100, Step=11/43, loss=9.1217, lr=1e-06, time_each_step=0.47s, eta=0:2:13\n",
      "2021-08-13 17:24:42 [INFO]\t[TRAIN] Epoch=94/100, Step=13/43, loss=15.624071, lr=1e-06, time_each_step=0.47s, eta=0:2:12\n",
      "2021-08-13 17:24:42 [INFO]\t[TRAIN] Epoch=94/100, Step=15/43, loss=10.828022, lr=1e-06, time_each_step=0.49s, eta=0:2:12\n",
      "2021-08-13 17:24:43 [INFO]\t[TRAIN] Epoch=94/100, Step=17/43, loss=11.859489, lr=1e-06, time_each_step=0.5s, eta=0:2:11\n",
      "2021-08-13 17:24:44 [INFO]\t[TRAIN] Epoch=94/100, Step=19/43, loss=11.370213, lr=1e-06, time_each_step=0.52s, eta=0:2:10\n",
      "2021-08-13 17:24:44 [INFO]\t[TRAIN] Epoch=94/100, Step=21/43, loss=5.73047, lr=1e-06, time_each_step=0.31s, eta=0:2:5\n",
      "2021-08-13 17:24:44 [INFO]\t[TRAIN] Epoch=94/100, Step=23/43, loss=10.126057, lr=1e-06, time_each_step=0.31s, eta=0:2:4\n",
      "2021-08-13 17:24:45 [INFO]\t[TRAIN] Epoch=94/100, Step=25/43, loss=7.87554, lr=1e-06, time_each_step=0.29s, eta=0:2:3\n",
      "2021-08-13 17:24:46 [INFO]\t[TRAIN] Epoch=94/100, Step=27/43, loss=9.81463, lr=1e-06, time_each_step=0.29s, eta=0:2:2\n",
      "2021-08-13 17:24:46 [INFO]\t[TRAIN] Epoch=94/100, Step=29/43, loss=9.532269, lr=1e-06, time_each_step=0.27s, eta=0:2:2\n",
      "2021-08-13 17:24:46 [INFO]\t[TRAIN] Epoch=94/100, Step=31/43, loss=9.475867, lr=1e-06, time_each_step=0.26s, eta=0:2:1\n",
      "2021-08-13 17:24:47 [INFO]\t[TRAIN] Epoch=94/100, Step=33/43, loss=12.327581, lr=1e-06, time_each_step=0.24s, eta=0:2:0\n",
      "2021-08-13 17:24:47 [INFO]\t[TRAIN] Epoch=94/100, Step=35/43, loss=9.955763, lr=1e-06, time_each_step=0.23s, eta=0:2:0\n",
      "2021-08-13 17:24:47 [INFO]\t[TRAIN] Epoch=94/100, Step=37/43, loss=7.83013, lr=1e-06, time_each_step=0.21s, eta=0:1:59\n",
      "2021-08-13 17:24:48 [INFO]\t[TRAIN] Epoch=94/100, Step=39/43, loss=7.54629, lr=1e-06, time_each_step=0.21s, eta=0:1:59\n",
      "2021-08-13 17:24:48 [INFO]\t[TRAIN] Epoch=94/100, Step=41/43, loss=8.704296, lr=1e-06, time_each_step=0.2s, eta=0:1:58\n",
      "2021-08-13 17:24:49 [INFO]\t[TRAIN] Epoch=94/100, Step=43/43, loss=10.096187, lr=1e-06, time_each_step=0.21s, eta=0:1:58\n",
      "2021-08-13 17:24:49 [INFO]\t[TRAIN] Epoch 94 finished, loss=10.504274, lr=1e-06 .\n",
      "2021-08-13 17:24:55 [INFO]\t[TRAIN] Epoch=95/100, Step=2/43, loss=8.581807, lr=1e-06, time_each_step=0.5s, eta=0:1:43\n",
      "2021-08-13 17:24:55 [INFO]\t[TRAIN] Epoch=95/100, Step=4/43, loss=9.978661, lr=1e-06, time_each_step=0.5s, eta=0:1:42\n",
      "2021-08-13 17:24:56 [INFO]\t[TRAIN] Epoch=95/100, Step=6/43, loss=11.598731, lr=1e-06, time_each_step=0.51s, eta=0:1:41\n",
      "2021-08-13 17:24:57 [INFO]\t[TRAIN] Epoch=95/100, Step=8/43, loss=17.410011, lr=1e-06, time_each_step=0.52s, eta=0:1:40\n",
      "2021-08-13 17:24:57 [INFO]\t[TRAIN] Epoch=95/100, Step=10/43, loss=7.48519, lr=1e-06, time_each_step=0.54s, eta=0:1:40\n",
      "2021-08-13 17:24:58 [INFO]\t[TRAIN] Epoch=95/100, Step=12/43, loss=12.463861, lr=1e-06, time_each_step=0.56s, eta=0:1:40\n",
      "2021-08-13 17:24:59 [INFO]\t[TRAIN] Epoch=95/100, Step=14/43, loss=9.402685, lr=1e-06, time_each_step=0.56s, eta=0:1:39\n",
      "2021-08-13 17:24:59 [INFO]\t[TRAIN] Epoch=95/100, Step=16/43, loss=7.534178, lr=1e-06, time_each_step=0.58s, eta=0:1:38\n",
      "2021-08-13 17:25:00 [INFO]\t[TRAIN] Epoch=95/100, Step=18/43, loss=11.288141, lr=1e-06, time_each_step=0.59s, eta=0:1:37\n",
      "2021-08-13 17:25:00 [INFO]\t[TRAIN] Epoch=95/100, Step=20/43, loss=7.60102, lr=1e-06, time_each_step=0.59s, eta=0:1:36\n",
      "2021-08-13 17:25:01 [INFO]\t[TRAIN] Epoch=95/100, Step=22/43, loss=11.437781, lr=1e-06, time_each_step=0.29s, eta=0:1:28\n",
      "2021-08-13 17:25:01 [INFO]\t[TRAIN] Epoch=95/100, Step=24/43, loss=10.250746, lr=1e-06, time_each_step=0.28s, eta=0:1:28\n",
      "2021-08-13 17:25:01 [INFO]\t[TRAIN] Epoch=95/100, Step=26/43, loss=10.460572, lr=1e-06, time_each_step=0.27s, eta=0:1:27\n",
      "2021-08-13 17:25:02 [INFO]\t[TRAIN] Epoch=95/100, Step=28/43, loss=10.78284, lr=1e-06, time_each_step=0.26s, eta=0:1:26\n",
      "2021-08-13 17:25:02 [INFO]\t[TRAIN] Epoch=95/100, Step=30/43, loss=7.092999, lr=1e-06, time_each_step=0.24s, eta=0:1:25\n",
      "2021-08-13 17:25:03 [INFO]\t[TRAIN] Epoch=95/100, Step=32/43, loss=9.858379, lr=1e-06, time_each_step=0.22s, eta=0:1:25\n",
      "2021-08-13 17:25:03 [INFO]\t[TRAIN] Epoch=95/100, Step=34/43, loss=8.847889, lr=1e-06, time_each_step=0.22s, eta=0:1:24\n",
      "2021-08-13 17:25:03 [INFO]\t[TRAIN] Epoch=95/100, Step=36/43, loss=9.502952, lr=1e-06, time_each_step=0.19s, eta=0:1:24\n",
      "2021-08-13 17:25:03 [INFO]\t[TRAIN] Epoch=95/100, Step=38/43, loss=10.909964, lr=1e-06, time_each_step=0.18s, eta=0:1:23\n",
      "2021-08-13 17:25:04 [INFO]\t[TRAIN] Epoch=95/100, Step=40/43, loss=12.692773, lr=1e-06, time_each_step=0.17s, eta=0:1:23\n",
      "2021-08-13 17:25:04 [INFO]\t[TRAIN] Epoch=95/100, Step=42/43, loss=14.886673, lr=1e-06, time_each_step=0.18s, eta=0:1:22\n",
      "2021-08-13 17:25:05 [INFO]\t[TRAIN] Epoch 95 finished, loss=10.259136, lr=1e-06 .\n",
      "2021-08-13 17:25:09 [INFO]\t[TRAIN] Epoch=96/100, Step=1/43, loss=7.851884, lr=1e-06, time_each_step=0.42s, eta=0:1:29\n",
      "2021-08-13 17:25:10 [INFO]\t[TRAIN] Epoch=96/100, Step=3/43, loss=13.435047, lr=1e-06, time_each_step=0.42s, eta=0:1:28\n",
      "2021-08-13 17:25:11 [INFO]\t[TRAIN] Epoch=96/100, Step=5/43, loss=7.735043, lr=1e-06, time_each_step=0.45s, eta=0:1:28\n",
      "2021-08-13 17:25:11 [INFO]\t[TRAIN] Epoch=96/100, Step=7/43, loss=8.143683, lr=1e-06, time_each_step=0.45s, eta=0:1:28\n",
      "2021-08-13 17:25:12 [INFO]\t[TRAIN] Epoch=96/100, Step=9/43, loss=9.753181, lr=1e-06, time_each_step=0.47s, eta=0:1:27\n",
      "2021-08-13 17:25:13 [INFO]\t[TRAIN] Epoch=96/100, Step=11/43, loss=8.970613, lr=1e-06, time_each_step=0.5s, eta=0:1:27\n",
      "2021-08-13 17:25:13 [INFO]\t[TRAIN] Epoch=96/100, Step=13/43, loss=9.598568, lr=1e-06, time_each_step=0.52s, eta=0:1:27\n",
      "2021-08-13 17:25:14 [INFO]\t[TRAIN] Epoch=96/100, Step=15/43, loss=7.510186, lr=1e-06, time_each_step=0.53s, eta=0:1:26\n",
      "2021-08-13 17:25:15 [INFO]\t[TRAIN] Epoch=96/100, Step=17/43, loss=8.904394, lr=1e-06, time_each_step=0.55s, eta=0:1:25\n",
      "2021-08-13 17:25:15 [INFO]\t[TRAIN] Epoch=96/100, Step=19/43, loss=8.469317, lr=1e-06, time_each_step=0.55s, eta=0:1:24\n",
      "2021-08-13 17:25:16 [INFO]\t[TRAIN] Epoch=96/100, Step=21/43, loss=11.938663, lr=1e-06, time_each_step=0.31s, eta=0:1:18\n",
      "2021-08-13 17:25:16 [INFO]\t[TRAIN] Epoch=96/100, Step=23/43, loss=13.427178, lr=1e-06, time_each_step=0.3s, eta=0:1:17\n",
      "2021-08-13 17:25:16 [INFO]\t[TRAIN] Epoch=96/100, Step=25/43, loss=7.789685, lr=1e-06, time_each_step=0.28s, eta=0:1:16\n",
      "2021-08-13 17:25:17 [INFO]\t[TRAIN] Epoch=96/100, Step=27/43, loss=7.929464, lr=1e-06, time_each_step=0.27s, eta=0:1:16\n",
      "2021-08-13 17:25:17 [INFO]\t[TRAIN] Epoch=96/100, Step=29/43, loss=12.253216, lr=1e-06, time_each_step=0.26s, eta=0:1:15\n",
      "2021-08-13 17:25:18 [INFO]\t[TRAIN] Epoch=96/100, Step=31/43, loss=9.021433, lr=1e-06, time_each_step=0.24s, eta=0:1:14\n",
      "2021-08-13 17:25:18 [INFO]\t[TRAIN] Epoch=96/100, Step=33/43, loss=9.007835, lr=1e-06, time_each_step=0.24s, eta=0:1:14\n",
      "2021-08-13 17:25:19 [INFO]\t[TRAIN] Epoch=96/100, Step=35/43, loss=9.283253, lr=1e-06, time_each_step=0.23s, eta=0:1:13\n",
      "2021-08-13 17:25:19 [INFO]\t[TRAIN] Epoch=96/100, Step=37/43, loss=10.493479, lr=1e-06, time_each_step=0.22s, eta=0:1:13\n",
      "2021-08-13 17:25:20 [INFO]\t[TRAIN] Epoch=96/100, Step=39/43, loss=9.502189, lr=1e-06, time_each_step=0.21s, eta=0:1:12\n",
      "2021-08-13 17:25:20 [INFO]\t[TRAIN] Epoch=96/100, Step=41/43, loss=13.342179, lr=1e-06, time_each_step=0.22s, eta=0:1:12\n",
      "2021-08-13 17:25:21 [INFO]\t[TRAIN] Epoch=96/100, Step=43/43, loss=10.736245, lr=1e-06, time_each_step=0.23s, eta=0:1:11\n",
      "2021-08-13 17:25:21 [INFO]\t[TRAIN] Epoch 96 finished, loss=10.22499, lr=1e-06 .\n",
      "2021-08-13 17:25:26 [INFO]\t[TRAIN] Epoch=97/100, Step=2/43, loss=8.887225, lr=1e-06, time_each_step=0.49s, eta=0:1:15\n",
      "2021-08-13 17:25:27 [INFO]\t[TRAIN] Epoch=97/100, Step=4/43, loss=8.08408, lr=1e-06, time_each_step=0.5s, eta=0:1:14\n",
      "2021-08-13 17:25:27 [INFO]\t[TRAIN] Epoch=97/100, Step=6/43, loss=12.33602, lr=1e-06, time_each_step=0.52s, eta=0:1:14\n",
      "2021-08-13 17:25:28 [INFO]\t[TRAIN] Epoch=97/100, Step=8/43, loss=11.914505, lr=1e-06, time_each_step=0.54s, eta=0:1:14\n",
      "2021-08-13 17:25:29 [INFO]\t[TRAIN] Epoch=97/100, Step=10/43, loss=10.277005, lr=1e-06, time_each_step=0.54s, eta=0:1:12\n",
      "2021-08-13 17:25:30 [INFO]\t[TRAIN] Epoch=97/100, Step=12/43, loss=13.783794, lr=1e-06, time_each_step=0.55s, eta=0:1:12\n",
      "2021-08-13 17:25:31 [INFO]\t[TRAIN] Epoch=97/100, Step=14/43, loss=9.647673, lr=1e-06, time_each_step=0.57s, eta=0:1:11\n",
      "2021-08-13 17:25:31 [INFO]\t[TRAIN] Epoch=97/100, Step=16/43, loss=9.780164, lr=1e-06, time_each_step=0.57s, eta=0:1:10\n",
      "2021-08-13 17:25:32 [INFO]\t[TRAIN] Epoch=97/100, Step=18/43, loss=8.248081, lr=1e-06, time_each_step=0.58s, eta=0:1:9\n",
      "2021-08-13 17:25:32 [INFO]\t[TRAIN] Epoch=97/100, Step=20/43, loss=6.470942, lr=1e-06, time_each_step=0.58s, eta=0:1:8\n",
      "2021-08-13 17:25:33 [INFO]\t[TRAIN] Epoch=97/100, Step=22/43, loss=13.531358, lr=1e-06, time_each_step=0.32s, eta=0:1:1\n",
      "2021-08-13 17:25:33 [INFO]\t[TRAIN] Epoch=97/100, Step=24/43, loss=11.731975, lr=1e-06, time_each_step=0.32s, eta=0:1:1\n",
      "2021-08-13 17:25:34 [INFO]\t[TRAIN] Epoch=97/100, Step=26/43, loss=11.847548, lr=1e-06, time_each_step=0.31s, eta=0:1:0\n",
      "2021-08-13 17:25:34 [INFO]\t[TRAIN] Epoch=97/100, Step=28/43, loss=14.109093, lr=1e-06, time_each_step=0.28s, eta=0:0:59\n",
      "2021-08-13 17:25:35 [INFO]\t[TRAIN] Epoch=97/100, Step=30/43, loss=9.749394, lr=1e-06, time_each_step=0.27s, eta=0:0:58\n",
      "2021-08-13 17:25:35 [INFO]\t[TRAIN] Epoch=97/100, Step=32/43, loss=11.075947, lr=1e-06, time_each_step=0.26s, eta=0:0:57\n",
      "2021-08-13 17:25:35 [INFO]\t[TRAIN] Epoch=97/100, Step=34/43, loss=8.39827, lr=1e-06, time_each_step=0.23s, eta=0:0:57\n",
      "2021-08-13 17:25:36 [INFO]\t[TRAIN] Epoch=97/100, Step=36/43, loss=11.658318, lr=1e-06, time_each_step=0.23s, eta=0:0:56\n",
      "2021-08-13 17:25:36 [INFO]\t[TRAIN] Epoch=97/100, Step=38/43, loss=10.709681, lr=1e-06, time_each_step=0.22s, eta=0:0:56\n",
      "2021-08-13 17:25:36 [INFO]\t[TRAIN] Epoch=97/100, Step=40/43, loss=13.042715, lr=1e-06, time_each_step=0.21s, eta=0:0:55\n",
      "2021-08-13 17:25:37 [INFO]\t[TRAIN] Epoch=97/100, Step=42/43, loss=9.376188, lr=1e-06, time_each_step=0.2s, eta=0:0:55\n",
      "2021-08-13 17:25:37 [INFO]\t[TRAIN] Epoch 97 finished, loss=10.174696, lr=1e-06 .\n",
      "2021-08-13 17:25:44 [INFO]\t[TRAIN] Epoch=98/100, Step=1/43, loss=9.136751, lr=1e-06, time_each_step=0.56s, eta=0:1:3\n",
      "2021-08-13 17:25:45 [INFO]\t[TRAIN] Epoch=98/100, Step=3/43, loss=16.791618, lr=1e-06, time_each_step=0.57s, eta=0:1:3\n",
      "2021-08-13 17:25:45 [INFO]\t[TRAIN] Epoch=98/100, Step=5/43, loss=8.163273, lr=1e-06, time_each_step=0.57s, eta=0:1:1\n",
      "2021-08-13 17:25:46 [INFO]\t[TRAIN] Epoch=98/100, Step=7/43, loss=8.837205, lr=1e-06, time_each_step=0.59s, eta=0:1:1\n",
      "2021-08-13 17:25:47 [INFO]\t[TRAIN] Epoch=98/100, Step=9/43, loss=12.026329, lr=1e-06, time_each_step=0.6s, eta=0:1:0\n",
      "2021-08-13 17:25:48 [INFO]\t[TRAIN] Epoch=98/100, Step=11/43, loss=9.95859, lr=1e-06, time_each_step=0.63s, eta=0:1:0\n",
      "2021-08-13 17:25:48 [INFO]\t[TRAIN] Epoch=98/100, Step=13/43, loss=7.051539, lr=1e-06, time_each_step=0.63s, eta=0:0:59\n",
      "2021-08-13 17:25:49 [INFO]\t[TRAIN] Epoch=98/100, Step=15/43, loss=16.405128, lr=1e-06, time_each_step=0.65s, eta=0:0:58\n",
      "2021-08-13 17:25:49 [INFO]\t[TRAIN] Epoch=98/100, Step=17/43, loss=8.497727, lr=1e-06, time_each_step=0.65s, eta=0:0:57\n",
      "2021-08-13 17:25:50 [INFO]\t[TRAIN] Epoch=98/100, Step=19/43, loss=16.186293, lr=1e-06, time_each_step=0.66s, eta=0:0:55\n",
      "2021-08-13 17:25:50 [INFO]\t[TRAIN] Epoch=98/100, Step=21/43, loss=11.483445, lr=1e-06, time_each_step=0.3s, eta=0:0:46\n",
      "2021-08-13 17:25:51 [INFO]\t[TRAIN] Epoch=98/100, Step=23/43, loss=12.267632, lr=1e-06, time_each_step=0.28s, eta=0:0:45\n",
      "2021-08-13 17:25:51 [INFO]\t[TRAIN] Epoch=98/100, Step=25/43, loss=7.161152, lr=1e-06, time_each_step=0.28s, eta=0:0:45\n",
      "2021-08-13 17:25:52 [INFO]\t[TRAIN] Epoch=98/100, Step=27/43, loss=7.491077, lr=1e-06, time_each_step=0.26s, eta=0:0:44\n",
      "2021-08-13 17:25:52 [INFO]\t[TRAIN] Epoch=98/100, Step=29/43, loss=9.224115, lr=1e-06, time_each_step=0.24s, eta=0:0:43\n",
      "2021-08-13 17:25:52 [INFO]\t[TRAIN] Epoch=98/100, Step=31/43, loss=9.324692, lr=1e-06, time_each_step=0.23s, eta=0:0:42\n",
      "2021-08-13 17:25:53 [INFO]\t[TRAIN] Epoch=98/100, Step=33/43, loss=8.643152, lr=1e-06, time_each_step=0.22s, eta=0:0:42\n",
      "2021-08-13 17:25:53 [INFO]\t[TRAIN] Epoch=98/100, Step=35/43, loss=10.091604, lr=1e-06, time_each_step=0.2s, eta=0:0:41\n",
      "2021-08-13 17:25:53 [INFO]\t[TRAIN] Epoch=98/100, Step=37/43, loss=8.709045, lr=1e-06, time_each_step=0.19s, eta=0:0:41\n",
      "2021-08-13 17:25:54 [INFO]\t[TRAIN] Epoch=98/100, Step=39/43, loss=8.335136, lr=1e-06, time_each_step=0.19s, eta=0:0:40\n",
      "2021-08-13 17:25:54 [INFO]\t[TRAIN] Epoch=98/100, Step=41/43, loss=9.970422, lr=1e-06, time_each_step=0.2s, eta=0:0:40\n",
      "2021-08-13 17:25:55 [INFO]\t[TRAIN] Epoch=98/100, Step=43/43, loss=11.507908, lr=1e-06, time_each_step=0.2s, eta=0:0:40\n",
      "2021-08-13 17:25:55 [INFO]\t[TRAIN] Epoch 98 finished, loss=10.403827, lr=1e-06 .\n",
      "2021-08-13 17:26:00 [INFO]\t[TRAIN] Epoch=99/100, Step=2/43, loss=11.14192, lr=1e-06, time_each_step=0.44s, eta=0:0:43\n",
      "2021-08-13 17:26:00 [INFO]\t[TRAIN] Epoch=99/100, Step=4/43, loss=9.888863, lr=1e-06, time_each_step=0.44s, eta=0:0:42\n",
      "2021-08-13 17:26:01 [INFO]\t[TRAIN] Epoch=99/100, Step=6/43, loss=15.680059, lr=1e-06, time_each_step=0.47s, eta=0:0:42\n",
      "2021-08-13 17:26:02 [INFO]\t[TRAIN] Epoch=99/100, Step=8/43, loss=12.20472, lr=1e-06, time_each_step=0.47s, eta=0:0:41\n",
      "2021-08-13 17:26:02 [INFO]\t[TRAIN] Epoch=99/100, Step=10/43, loss=9.777716, lr=1e-06, time_each_step=0.49s, eta=0:0:41\n",
      "2021-08-13 17:26:03 [INFO]\t[TRAIN] Epoch=99/100, Step=12/43, loss=8.185202, lr=1e-06, time_each_step=0.5s, eta=0:0:40\n",
      "2021-08-13 17:26:03 [INFO]\t[TRAIN] Epoch=99/100, Step=14/43, loss=10.949555, lr=1e-06, time_each_step=0.51s, eta=0:0:40\n",
      "2021-08-13 17:26:04 [INFO]\t[TRAIN] Epoch=99/100, Step=16/43, loss=9.044436, lr=1e-06, time_each_step=0.52s, eta=0:0:39\n",
      "2021-08-13 17:26:05 [INFO]\t[TRAIN] Epoch=99/100, Step=18/43, loss=7.233221, lr=1e-06, time_each_step=0.52s, eta=0:0:38\n",
      "2021-08-13 17:26:05 [INFO]\t[TRAIN] Epoch=99/100, Step=20/43, loss=10.396119, lr=1e-06, time_each_step=0.53s, eta=0:0:37\n",
      "2021-08-13 17:26:06 [INFO]\t[TRAIN] Epoch=99/100, Step=22/43, loss=7.380694, lr=1e-06, time_each_step=0.3s, eta=0:0:31\n",
      "2021-08-13 17:26:06 [INFO]\t[TRAIN] Epoch=99/100, Step=24/43, loss=9.597067, lr=1e-06, time_each_step=0.29s, eta=0:0:30\n",
      "2021-08-13 17:26:07 [INFO]\t[TRAIN] Epoch=99/100, Step=26/43, loss=10.734046, lr=1e-06, time_each_step=0.27s, eta=0:0:30\n",
      "2021-08-13 17:26:07 [INFO]\t[TRAIN] Epoch=99/100, Step=28/43, loss=9.173599, lr=1e-06, time_each_step=0.27s, eta=0:0:29\n",
      "2021-08-13 17:26:08 [INFO]\t[TRAIN] Epoch=99/100, Step=30/43, loss=7.516982, lr=1e-06, time_each_step=0.26s, eta=0:0:28\n",
      "2021-08-13 17:26:08 [INFO]\t[TRAIN] Epoch=99/100, Step=32/43, loss=11.008103, lr=1e-06, time_each_step=0.24s, eta=0:0:28\n",
      "2021-08-13 17:26:08 [INFO]\t[TRAIN] Epoch=99/100, Step=34/43, loss=9.767746, lr=1e-06, time_each_step=0.23s, eta=0:0:27\n",
      "2021-08-13 17:26:09 [INFO]\t[TRAIN] Epoch=99/100, Step=36/43, loss=6.996872, lr=1e-06, time_each_step=0.23s, eta=0:0:27\n",
      "2021-08-13 17:26:09 [INFO]\t[TRAIN] Epoch=99/100, Step=38/43, loss=8.603204, lr=1e-06, time_each_step=0.22s, eta=0:0:26\n",
      "2021-08-13 17:26:10 [INFO]\t[TRAIN] Epoch=99/100, Step=40/43, loss=8.304338, lr=1e-06, time_each_step=0.22s, eta=0:0:26\n",
      "2021-08-13 17:26:10 [INFO]\t[TRAIN] Epoch=99/100, Step=42/43, loss=9.94698, lr=1e-06, time_each_step=0.23s, eta=0:0:25\n",
      "2021-08-13 17:26:11 [INFO]\t[TRAIN] Epoch 99 finished, loss=9.927329, lr=1e-06 .\n",
      "2021-08-13 17:26:15 [INFO]\t[TRAIN] Epoch=100/100, Step=1/43, loss=11.801114, lr=1e-06, time_each_step=0.44s, eta=0:0:26\n",
      "2021-08-13 17:26:16 [INFO]\t[TRAIN] Epoch=100/100, Step=3/43, loss=7.503621, lr=1e-06, time_each_step=0.45s, eta=0:0:25\n",
      "2021-08-13 17:26:16 [INFO]\t[TRAIN] Epoch=100/100, Step=5/43, loss=15.120303, lr=1e-06, time_each_step=0.45s, eta=0:0:24\n",
      "2021-08-13 17:26:17 [INFO]\t[TRAIN] Epoch=100/100, Step=7/43, loss=10.607663, lr=1e-06, time_each_step=0.46s, eta=0:0:24\n",
      "2021-08-13 17:26:18 [INFO]\t[TRAIN] Epoch=100/100, Step=9/43, loss=10.73613, lr=1e-06, time_each_step=0.49s, eta=0:0:24\n",
      "2021-08-13 17:26:18 [INFO]\t[TRAIN] Epoch=100/100, Step=11/43, loss=13.661339, lr=1e-06, time_each_step=0.51s, eta=0:0:23\n",
      "2021-08-13 17:26:19 [INFO]\t[TRAIN] Epoch=100/100, Step=13/43, loss=8.723086, lr=1e-06, time_each_step=0.5s, eta=0:0:22\n",
      "2021-08-13 17:26:19 [INFO]\t[TRAIN] Epoch=100/100, Step=15/43, loss=10.777915, lr=1e-06, time_each_step=0.51s, eta=0:0:21\n",
      "2021-08-13 17:26:20 [INFO]\t[TRAIN] Epoch=100/100, Step=17/43, loss=7.735835, lr=1e-06, time_each_step=0.51s, eta=0:0:20\n",
      "2021-08-13 17:26:20 [INFO]\t[TRAIN] Epoch=100/100, Step=19/43, loss=9.857477, lr=1e-06, time_each_step=0.51s, eta=0:0:19\n",
      "2021-08-13 17:26:21 [INFO]\t[TRAIN] Epoch=100/100, Step=21/43, loss=10.248216, lr=1e-06, time_each_step=0.29s, eta=0:0:13\n",
      "2021-08-13 17:26:21 [INFO]\t[TRAIN] Epoch=100/100, Step=23/43, loss=7.975145, lr=1e-06, time_each_step=0.29s, eta=0:0:13\n",
      "2021-08-13 17:26:22 [INFO]\t[TRAIN] Epoch=100/100, Step=25/43, loss=12.856457, lr=1e-06, time_each_step=0.29s, eta=0:0:12\n",
      "2021-08-13 17:26:23 [INFO]\t[TRAIN] Epoch=100/100, Step=27/43, loss=12.126869, lr=1e-06, time_each_step=0.29s, eta=0:0:12\n",
      "2021-08-13 17:26:23 [INFO]\t[TRAIN] Epoch=100/100, Step=29/43, loss=9.849993, lr=1e-06, time_each_step=0.27s, eta=0:0:11\n",
      "2021-08-13 17:26:24 [INFO]\t[TRAIN] Epoch=100/100, Step=31/43, loss=9.182917, lr=1e-06, time_each_step=0.26s, eta=0:0:10\n",
      "2021-08-13 17:26:24 [INFO]\t[TRAIN] Epoch=100/100, Step=33/43, loss=10.96143, lr=1e-06, time_each_step=0.27s, eta=0:0:10\n",
      "2021-08-13 17:26:24 [INFO]\t[TRAIN] Epoch=100/100, Step=35/43, loss=9.518287, lr=1e-06, time_each_step=0.25s, eta=0:0:9\n",
      "2021-08-13 17:26:25 [INFO]\t[TRAIN] Epoch=100/100, Step=37/43, loss=8.658, lr=1e-06, time_each_step=0.25s, eta=0:0:9\n",
      "2021-08-13 17:26:25 [INFO]\t[TRAIN] Epoch=100/100, Step=39/43, loss=10.172385, lr=1e-06, time_each_step=0.24s, eta=0:0:8\n",
      "2021-08-13 17:26:26 [INFO]\t[TRAIN] Epoch=100/100, Step=41/43, loss=9.506551, lr=1e-06, time_each_step=0.24s, eta=0:0:8\n",
      "2021-08-13 17:26:26 [INFO]\t[TRAIN] Epoch=100/100, Step=43/43, loss=11.535403, lr=1e-06, time_each_step=0.24s, eta=0:0:7\n",
      "2021-08-13 17:26:26 [INFO]\t[TRAIN] Epoch 100 finished, loss=10.394317, lr=1e-06 .\n",
      "2021-08-13 17:26:26 [INFO]\tStart to evaluating(total_samples=98, total_steps=13)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:08<00:00,  1.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:26:35 [INFO]\t[EVAL] Finished, Epoch=100, bbox_map=62.619741 .\n",
      "2021-08-13 17:26:37 [INFO]\tModel saved in output/yolov3_darknet53/epoch_100.\n",
      "2021-08-13 17:26:37 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_80, bbox_map=63.75984622249753\n"
     ]
    }
   ],
   "source": [
    "# 环境变量配置，用于控制是否使用GPU\r\n",
    "# 说明文档：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu\r\n",
    "import os\r\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\r\n",
    "\r\n",
    "from paddlex.det import transforms\r\n",
    "import paddlex as pdx\r\n",
    "\r\n",
    "# 定义训练和验证时的transforms\r\n",
    "# API说明 https://paddlex.readthedocs.io/zh_CN/develop/apis/transforms/det_transforms.html\r\n",
    "train_transforms = transforms.Compose([\r\n",
    "    transforms.MixupImage(mixup_epoch=250), transforms.RandomDistort(),\r\n",
    "    transforms.RandomExpand(), transforms.RandomCrop(), transforms.Resize(\r\n",
    "        target_size=608, interp='RANDOM'), transforms.RandomHorizontalFlip(),\r\n",
    "    transforms.Normalize()\r\n",
    "])\r\n",
    "\r\n",
    "eval_transforms = transforms.Compose([\r\n",
    "    transforms.Resize(\r\n",
    "        target_size=608, interp='CUBIC'), transforms.Normalize()\r\n",
    "])\r\n",
    "\r\n",
    "# 定义训练和验证所用的数据集\r\n",
    "# API说明：https://paddlex.readthedocs.io/zh_CN/develop/apis/datasets.html#paddlex-datasets-vocdetection\r\n",
    "train_dataset = pdx.datasets.VOCDetection(\r\n",
    "    data_dir='MyDataset/fire',\r\n",
    "    file_list='MyDataset/fire/train_list.txt',\r\n",
    "    label_list='MyDataset/fire/labels.txt',\r\n",
    "    transforms=train_transforms,\r\n",
    "    shuffle=True)\r\n",
    "eval_dataset = pdx.datasets.VOCDetection(\r\n",
    "    data_dir='MyDataset/fire',\r\n",
    "    file_list='MyDataset/fire/val_list.txt',\r\n",
    "    label_list='MyDataset/fire/labels.txt',\r\n",
    "    transforms=eval_transforms)\r\n",
    "\r\n",
    "# 初始化模型，并进行训练\r\n",
    "# 可使用VisualDL查看训练指标，参考https://paddlex.readthedocs.io/zh_CN/develop/train/visualdl.html\r\n",
    "num_classes = len(train_dataset.labels)\r\n",
    "\r\n",
    "# API说明: https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#paddlex-det-yolov3\r\n",
    "model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')\r\n",
    "\r\n",
    "# API说明: https://paddlex.readthedocs.io/zh_CN/develop/apis/models/detection.html#train\r\n",
    "# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html\r\n",
    "model.train(\r\n",
    "    num_epochs=100,\r\n",
    "    train_dataset=train_dataset,\r\n",
    "    train_batch_size=8,\r\n",
    "    eval_dataset=eval_dataset,\r\n",
    "    learning_rate=0.000125,\r\n",
    "    warmup_steps=1000,\r\n",
    "    warmup_start_lr=0.0,\r\n",
    "    save_interval_epochs=10,\r\n",
    "    lr_decay_epochs=[50, 80],\r\n",
    "    save_dir='output/yolov3_darknet53'\r\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W0813 17:29:23.092429 32626 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0813 17:29:23.097010 32626 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "2021-08-13 17:29:29 [INFO]\tModel[YOLOv3] loaded.\n",
      "2021-08-13 17:29:30 [INFO]\tModel for inference deploy saved in ./inference_model.\n"
     ]
    }
   ],
   "source": [
    "#导出模型\r\n",
    "!paddlex --export_inference --model_dir=./output/yolov3_darknet53/epoch_100 --save_dir=./inference_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 17:42:19 [INFO]\tThe visualized result is saved as ./output/visualize_101.jpg\n"
     ]
    }
   ],
   "source": [
    "# 单张图片预测\r\n",
    "import paddlex as pdx\r\n",
    "predictor = pdx.deploy.Predictor('./inference_model')\r\n",
    "image_name='MyDataset/fire/JPEGImages/101.jpg'\r\n",
    "result = predictor.predict(image=image_name)\r\n",
    "pdx.det.visualize(image_name, result, threshold=0.2, save_dir='./output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\r\n",
    "%matplotlib inline\r\n",
    "import matplotlib.pyplot as plt # plt 用于显示图片\r\n",
    "import numpy as np\r\n",
    "import cv2\r\n",
    "\r\n",
    "# 读取原始图片\r\n",
    "origin_pic = cv2.imread('MyDataset/fire/JPEGImages/101.jpg')\r\n",
    "origin_pic = cv2.cvtColor(origin_pic, cv2.COLOR_BGR2RGB)\r\n",
    "plt.imshow(origin_pic)\r\n",
    "plt.axis('off') # 不显示坐标轴\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**PaddleHub模型部署**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/\n",
      "Requirement already up-to-date: paddlehub in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: easydict in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (1.9)\n",
      "Requirement already satisfied, skipping upgrade: gitpython in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (3.1.14)\n",
      "Requirement already satisfied, skipping upgrade: Pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (7.1.2)\n",
      "Requirement already satisfied, skipping upgrade: paddle2onnx>=0.5.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (0.7)\n",
      "Requirement already satisfied, skipping upgrade: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (4.1.1.26)\n",
      "Requirement already satisfied, skipping upgrade: gunicorn>=19.10.0; sys_platform != \"win32\" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (20.0.4)\n",
      "Requirement already satisfied, skipping upgrade: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (4.36.1)\n",
      "Requirement already satisfied, skipping upgrade: packaging in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (20.9)\n",
      "Requirement already satisfied, skipping upgrade: colorama in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (0.4.4)\n",
      "Requirement already satisfied, skipping upgrade: visualdl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (2.2.0)\n",
      "Requirement already satisfied, skipping upgrade: rarfile in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (3.1)\n",
      "Requirement already satisfied, skipping upgrade: filelock in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (3.0.12)\n",
      "Requirement already satisfied, skipping upgrade: paddlenlp>=2.0.0rc5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (2.0.7)\n",
      "Requirement already satisfied, skipping upgrade: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (2.2.3)\n",
      "Requirement already satisfied, skipping upgrade: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (1.20.3)\n",
      "Requirement already satisfied, skipping upgrade: colorlog in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (4.1.0)\n",
      "Requirement already satisfied, skipping upgrade: pyzmq in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (18.1.1)\n",
      "Requirement already satisfied, skipping upgrade: flask>=1.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (1.1.1)\n",
      "Requirement already satisfied, skipping upgrade: pyyaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub) (5.1.2)\n",
      "Requirement already satisfied, skipping upgrade: gitdb<5,>=4.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from gitpython->paddlehub) (4.0.5)\n",
      "Requirement already satisfied, skipping upgrade: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle2onnx>=0.5.1->paddlehub) (1.15.0)\n",
      "Requirement already satisfied, skipping upgrade: protobuf in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddle2onnx>=0.5.1->paddlehub) (3.14.0)\n",
      "Requirement already satisfied, skipping upgrade: setuptools>=3.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from gunicorn>=19.10.0; sys_platform != \"win32\"->paddlehub) (56.2.0)\n",
      "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from packaging->paddlehub) (2.4.2)\n",
      "Requirement already satisfied, skipping upgrade: Flask-Babel>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (1.0.0)\n",
      "Requirement already satisfied, skipping upgrade: flake8>=3.7.9 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (3.8.2)\n",
      "Requirement already satisfied, skipping upgrade: pre-commit in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (1.21.0)\n",
      "Requirement already satisfied, skipping upgrade: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (2.22.0)\n",
      "Requirement already satisfied, skipping upgrade: pandas in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (1.1.5)\n",
      "Requirement already satisfied, skipping upgrade: bce-python-sdk in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (0.8.53)\n",
      "Requirement already satisfied, skipping upgrade: shellcheck-py in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from visualdl>=2.0.0->paddlehub) (0.7.1.1)\n",
      "Requirement already satisfied, skipping upgrade: multiprocess in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp>=2.0.0rc5->paddlehub) (0.70.11.1)\n",
      "Requirement already satisfied, skipping upgrade: jieba in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp>=2.0.0rc5->paddlehub) (0.42.1)\n",
      "Requirement already satisfied, skipping upgrade: seqeval in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp>=2.0.0rc5->paddlehub) (1.2.2)\n",
      "Requirement already satisfied, skipping upgrade: h5py in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp>=2.0.0rc5->paddlehub) (2.9.0)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->paddlehub) (2.8.0)\n",
      "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->paddlehub) (0.10.0)\n",
      "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->paddlehub) (1.1.0)\n",
      "Requirement already satisfied, skipping upgrade: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->paddlehub) (2019.3)\n",
      "Requirement already satisfied, skipping upgrade: Werkzeug>=0.15 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.0->paddlehub) (0.16.0)\n",
      "Requirement already satisfied, skipping upgrade: itsdangerous>=0.24 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.0->paddlehub) (1.1.0)\n",
      "Requirement already satisfied, skipping upgrade: click>=5.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.0->paddlehub) (7.0)\n",
      "Requirement already satisfied, skipping upgrade: Jinja2>=2.10.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flask>=1.1.0->paddlehub) (2.10.1)\n",
      "Requirement already satisfied, skipping upgrade: smmap<4,>=3.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from gitdb<5,>=4.0.1->gitpython->paddlehub) (3.0.5)\n",
      "Requirement already satisfied, skipping upgrade: Babel>=2.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Flask-Babel>=1.0.0->visualdl>=2.0.0->paddlehub) (2.8.0)\n",
      "Requirement already satisfied, skipping upgrade: pycodestyle<2.7.0,>=2.6.0a1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlehub) (2.6.0)\n",
      "Requirement already satisfied, skipping upgrade: mccabe<0.7.0,>=0.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlehub) (0.6.1)\n",
      "Requirement already satisfied, skipping upgrade: pyflakes<2.3.0,>=2.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlehub) (2.2.0)\n",
      "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl>=2.0.0->paddlehub) (0.23)\n",
      "Requirement already satisfied, skipping upgrade: virtualenv>=15.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlehub) (16.7.9)\n",
      "Requirement already satisfied, skipping upgrade: nodeenv>=0.11.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlehub) (1.3.4)\n",
      "Requirement already satisfied, skipping upgrade: identify>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlehub) (1.4.10)\n",
      "Requirement already satisfied, skipping upgrade: aspy.yaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlehub) (1.3.0)\n",
      "Requirement already satisfied, skipping upgrade: toml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlehub) (0.10.0)\n",
      "Requirement already satisfied, skipping upgrade: cfgv>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl>=2.0.0->paddlehub) (2.0.1)\n",
      "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlehub) (1.25.6)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlehub) (2019.9.11)\n",
      "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlehub) (2.8)\n",
      "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->paddlehub) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: future>=0.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl>=2.0.0->paddlehub) (0.18.0)\n",
      "Requirement already satisfied, skipping upgrade: pycryptodome>=3.8.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from bce-python-sdk->visualdl>=2.0.0->paddlehub) (3.9.9)\n",
      "Requirement already satisfied, skipping upgrade: dill>=0.3.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from multiprocess->paddlenlp>=2.0.0rc5->paddlehub) (0.3.3)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn>=0.21.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from seqeval->paddlenlp>=2.0.0rc5->paddlehub) (0.24.2)\n",
      "Requirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.10.1->flask>=1.1.0->paddlehub) (1.1.1)\n",
      "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata; python_version < \"3.8\"->flake8>=3.7.9->visualdl>=2.0.0->paddlehub) (0.6.0)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->paddlenlp>=2.0.0rc5->paddlehub) (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->paddlenlp>=2.0.0rc5->paddlehub) (0.14.1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.19.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->paddlenlp>=2.0.0rc5->paddlehub) (1.6.3)\n",
      "Requirement already satisfied, skipping upgrade: more-itertools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from zipp>=0.5->importlib-metadata; python_version < \"3.8\"->flake8>=3.7.9->visualdl>=2.0.0->paddlehub) (7.2.0)\n"
     ]
    }
   ],
   "source": [
    "#安装PaddleHub\r\n",
    "!pip install paddlehub -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "The converted module is stored in `firedet_1628847859.0540247`.\n"
     ]
    }
   ],
   "source": [
    "#PaddleX转PaddleHub\r\n",
    "!hub convert --model_dir inference_model \\\r\n",
    "              --module_name firedet \\\r\n",
    "              --module_version 1.0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "Decompress firedet_1628847859.0540247/firedet.tar.gz\n",
      "[##################################################] 100.00%\n",
      "[2021-08-13 17:45:10,094] [    INFO] - Successfully installed firedet-1.0\n"
     ]
    }
   ],
   "source": [
    "# 模型安装 安装上面生成的压缩包\r\n",
    "!hub install firedet_1628847859.0540247/firedet.tar.gz\r\n",
    "#然后在终端启动服务，hub serving start -m firedet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2021-08-13 18:02:48 [INFO]\tThe visualized result is saved as ./output/visualize_101.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#效果测试\r\n",
    "%matplotlib inline\r\n",
    "import requests\r\n",
    "import json\r\n",
    "import cv2\r\n",
    "import base64\r\n",
    "import numpy as np\r\n",
    "import colorsys\r\n",
    "import warnings\r\n",
    "warnings.filterwarnings(\"ignore\")\r\n",
    "plt.figure(figsize=(12,8))\r\n",
    "\r\n",
    "def cv2_to_base64(image):\r\n",
    "    data = cv2.imencode('.jpg', image)[1]\r\n",
    "    return base64.b64encode(data.tostring()).decode('utf8')\r\n",
    "\r\n",
    "\r\n",
    "if __name__ == '__main__':\r\n",
    "    # 获取图片的base64编码格式\r\n",
    "    img1 = cv2_to_base64(cv2.imread(\"MyDataset/fire/JPEGImages/101.jpg\"))\r\n",
    "    data = {'images': [img1]}\r\n",
    "    # 指定content-type\r\n",
    "    headers = {\"Content-type\": \"application/json\"}\r\n",
    "    # 发送HTTP请求\r\n",
    "    url = \"http://0.0.0.0:8866/predict/hatdet\"\r\n",
    "    r = requests.post(url=url, headers=headers, data=json.dumps(data))\r\n",
    "\r\n",
    "    # 打印预测结果，注意，r.json()[\"results\"]本身就是一个数组，要取到对应图片的预测结果，需指定元素位置，如r.json()[\"results\"][0]\r\n",
    "    print(r.json()[\"results\"])\r\n",
    "    # 使用重写的visualize()方法完成预测结果后处理\r\n",
    "    # 显示第一张图片的预测效果\r\n",
    "    image = pdx.det.visualize(image_name, result, threshold=0.2, save_dir='./output')\r\n",
    "\r\n",
    "    image = cv2.imread('output/visualize_101.jpg')\r\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n",
    "    plt.imshow(image)\r\n",
    "    plt.axis('off') # 不显示坐标轴\r\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  <br>\n",
    "Please click [here ](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 2.1.2 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
